This chapter describes the complex linkages between climate change, food systems, internal migration and intermediary cities. Given their close socio-economic and geographic links with rural areas, intermediary cities will face ripple effects of climate change. These include disruptions of food systems, and changes in the scale and pattern of rural-to-urban internal migration. The chapter presents policy actions that can help build resilience in both rural areas and urban centres. It argues that preparing for rapid urbanisation and strengthening food systems requires territorial planning that accounts for the interdependence between intermediary cities and rural areas. The chapter concludes by stressing the need for effective dialogue across all levels of government and the importance for local governments of forging partnerships to strengthen their capacities and resources.
Intermediary Cities and Climate Change
3. The ripple effects of climate change on intermediary cities: Disruption of food systems and internal migration
Abstract
Climate change is increasingly disrupting the rural-urban interface of developing countries. Extreme climatic events and climate variability threaten the livelihoods of urban and rural households, as well as the infrastructure they rely on, according to the Food and Agriculture Organization and other experts (FAO et al., 2018[1]; Vermeulen et al, 2012[2]). In addition to directly affecting urban and rural areas, climate change could also disrupt the ways in which these areas interact with each other, namely though food systems and internal migration.
This chapter discusses the relationship between internal migration, food systems, climate change and intermediary cities. It aims to unpack the intricate and complex channels through which climate change affects both food systems and internal migration, while also providing a better understanding of the potential implications for intermediary cities.
The chapter is structured as follows. The first section provides an overview of how climate change affects the rural-urban interface. The second assesses how climate change affects food systems and the ripple effects on intermediary cities. The third explores the relationship between climate-induced migration and intermediary cities. The final section proposes a way forward and possible policy implications.
Climate change and the rural-urban interface
The rural-urban interface encompasses intermediary cities, small towns and rural areas. It hosts 5.5‑6 billion inhabitants worldwide (Berdegué et al., 2014[3]; Proctor, 2018[4]), as well as most of the world’s 500 million smallholder farmers (FAO, 2017[5]). Indeed, as a majority of the global population resides in these areas, the rural-urban interface encompasses most of humanity. As a very dynamic area, it is key for the well-being of a large share of the population since it acts as a facilitator for continuous bi-directional movements of people and goods, as well as services (financial, environmental, etc.).
Intermediary cities are at the core of the rural-urban interface. They function as nodes that connect social and economic activities and provide the critical mass necessary for the development of social, economic and cultural processes taking place across the rural-urban interface (Berdegué et al., 2014[3]; OECD/PSI, 2020[6]). Intermediary cities are distinct in their functions and closeness to rural areas. They facilitate rural-urban linkages by giving rural populations access to infrastructure, transportation and public services as well as enabling access to markets for agricultural producers (Berdegué et al., 2014[3]). As such, intermediary cities are connected to rural areas through different channels that support the continuous flow of goods, services and people.
Food systems constitute a main channel tying together rural areas and intermediary cities, especially in developing countries. This is because activities within food systems are spatially distributed across rural and urban territories. Rural areas are most often the main site of agricultural production, while activities in the midstream (processing, wholesale, transportation) and downstream (distribution, retailing and restaurants) of food supply chains usually take place in intermediary cities, small towns and peri-urban areas (Berdegué et al., 2014[3]; Proctor, 2018[4]; Reardon and Zilberman, 2018[7]).
Another important channel linking intermediary cities and rural areas is internal migration. Population movements along the rural-urban continuum shape the development of both intermediary cities and rural areas. Indeed, between 2000 and 2010, rural-to-urban migration accounted for almost half of global urban population growth (Tacoli et al., 2014[8]). Overall, rural-to-urban migration strengthens the linkages between rural and urban areas (Berdegué et al., 2014[3]), as well as serving as a means of diversification of livelihoods and income.
Food systems and internal migration are intrinsically interlinked. Population movements along the rural-urban interface often reflect changes or transformations in food systems. In particular, food systems are a critical source of employment for a large share of rural migrants. Although the upstream segment of food systems (agricultural production) accounts for a significant share of total employment, the mid- and downstream segments of agricultural value chains are an increasingly important source of livelihood for rural migrants (Allen et al., 2018[9]; Dury et al., 2019[10]). As such, changes in food systems and their respective labour markets are closely correlated with migration trends and population dynamics in many developing countries (Berdegué et al., 2014[3]).
Rural-to-urban migration also plays a key role in the transformation of food systems. It leads to an increase in the demand for high-value-added and processed food, resulting in further transformations of food systems by prompting changes in agricultural production, food processing, distribution, storage, etc. (Tacoli and Agergaard, 2017[11]). At the same time, the growth of the “food away from home” sector and higher wages in urban areas are important pull factors for rural-to-urban migration (FAO, 2017[12]). Key characteristics of urban areas, such as agglomeration effects, spatial proximity and higher wages, have played key roles in the growth of “food away from home”, making the sector attractive to urban and rural consumers alike and transforming food systems (OECD, 2021[13]), while also turning the industry into an attractive source of income diversification. As such, migration to intermediary cities creates large scope for farm and off-farm employment creation along agri-food value chains. Transformation of food systems can also facilitate seasonal or circular migration, which enables rural income diversification (Arslan, Egger and Winters, 2018[14]). Moreover, remittances resulting from internal migration can boost investment in the agricultural sector, further enabling rural households to hire seasonal wage labour to work in food production, including with high-value crops such as fruit and vegetables (Tacoli and Agergaard, 2017[11]). Thus, food systems and internal migration are closely interlinked, i.e. changes in one eventually lead to changes in the other.
Intermediary cities play a fundamental role in the transformation of both food systems and internal migration. These agglomerations provide the soft and hard infrastructure and the critical mass that is necessary to enable activities within food systems to take place; by doing so they facilitate mobility among rural households (Tacoli and Agergaard, 2017[11]). The growth in size and importance of intermediary cities is blurring the lines of rural and urban boundaries, further facilitating migration flows and transformation of food systems (Tacoli et al., 2014[8]). Moreover, the close links of intermediary cities to rural areas enable migrants to maintain their social ties and allow them to avoid the higher cost of migrating to larger metropolitan cities (Berdegué et al., 2014[3]). At the same time, the downstream activities of food systems found in intermediary cities provide a source of employment for women.
Climate change is expected to become a major disruptor of rural-urban dynamics
Climate change is expected to weaken rural-urban connectivity by disrupting the channels that connect these territories (Box 3.1). Indeed, climate change is proving to be a major disruptor of agricultural value chains. Climate phenomena can be classified as fast- and slow-onset events. Fast-onset events (such as floods, storms and cyclones) can lead to an immediate loss of livelihoods and basic infrastructure, which affects food systems and can force people to migrate. Food systems refer to the vast and interlinked networks of value-adding activities stretching from the farmer’s gate to the consumer’s plate: supply of production input, production, aggregation, processing, wholesaling, distribution and consumption (Nguyen H, 2018[15]; IFAD, 2021[16]). They also encompass the subsystems, policies and regulations that underpin the operation of the supply chains, including subsectors such as waste management and input supply (Nguyen H, 2018[15]). Their complex linkages make them particularly susceptible to climate-induced risks.
Box 3.1. How climate change disrupts the rural-urban interface
There is a growing consensus today that climate change disrupts linkages between rural and urban areas (Dasgupta et al., 2014[17]; UN-Habitat, 2017[18]; Jamshed et al., 2021[19]). Urban areas, especially small and medium-sized cities, are inherently dependent on surrounding rural areas for resources such as water, energy and food supply. Climate effects in rural areas such as droughts can trigger shortages in the supply of water and other resources to urban areas (Morton et al., 2014[20]). This was the case, for example, in Bulawayo, Zimbabwe’s second largest city, where rural droughts led to city water shortages (Mkandla, Van der Zaag and Sibanda, 2005[21]). Similarly, flooding in rural areas of Pakistan has affected rural-urban linkages, increasing the price of transportation to cities and the cost of agricultural inputs such as fertiliser and seeds (supplied from cities). Moreover, as the floods negatively affected rural livelihoods, there was increased migration towards cities by rural people looking for work (Jamshed et al., 2021[19]).
Climate change will also affect service provision across the rural-urban interface. In climate disasters such as floods, droughts and storms, rural areas may be left at a disadvantage in terms of services as urban needs are often prioritised and have larger visibility by local and national governments (Morton et al., 2014[20]). This stems from the fact that rural areas tend to be less well equipped than urban areas for adapting to climate threats (Steinberg, 2014[22]). The effects of climate change such as flooding on rural-urban linkages can leave vulnerable populations trapped in poverty, with loss of livelihoods. Indeed, the socio-economic characteristics of individuals and households in the rural-urban interface play a large role in their resilience to climate change. In Pakistan, for instance, rural households with a higher level of education and income were better able to connect with cities after floods and had better access to finance, information, goods and services (Jamshed et al., 2021[19]).
Slow-onset events (rainfall variability, seasonal variations, rise in temperatures, etc.) also have large negative implications for food systems operating across the rural-urban interface. They lead to a decline in agricultural yield and income, affecting rural livelihoods and forcing rural dwellers to adopt coping strategies. Over a long period, this may become a push factor for rural migration (Arslan, Egger and Winters, 2018[14]). Moreover, climate change presents serious risks to infrastructure, which can consequently disrupt the mid- and downstream segments of food systems. Damage to transportation and wholesale and processing facilities can cause food waste, loss of livelihoods and overall interruption of activities along food value chains (Vermeulen et al, 2012[2]). Climate change thus acts as a multiplier of vulnerabilities (Mbow et al., 2019[23]). Figure 3.1 depicts the spatial distribution of food systems and migration flows along the rural-urban interface, as well as changes that can be induced by climate change.
Intermediary cities, defined in this report as cities with a population of 50 000 to 1 million, will bear a significant share of the burden resulting from climatic shocks along the rural-urban interface. Intermediary cities, as well as many larger agglomerations, already face climate-induced challenges (rising seas, flooding, water stress, etc.) that impact the livelihoods and well-being of their population. However, intermediary cities in developing countries are usually characterised by weak governance, poor urban planning and limited infrastructure, with a large share of their inhabitants living in vulnerable socio-economic conditions (Roberts et al., 2016[24]; Roberts and Hohmann, 2014[25]). As outlined in Chapter 2 of this report, intermediary cities also tend to have lower levels of economic diversification and largely rely on one dominant sector such as agriculture, mining, etc. (Roberts et al., 2016[24]), which contributes to low local revenue mobilisation. Furthermore, intermediary cities often have limited capacity and authority to collect local revenue and invest in infrastructure and local services (Satterthwaite, 2016[26]), as in the case of Quelimane (Mozambique), described below in this chapter.
Not only are intermediary cities are particularly vulnerable to climate change, but they are also exposed to the effects of climate change on surrounding rural areas. In other words, intermediary cities will face the compounded effects of climate change across the rural-urban interface. For instance, as climate change prompts increasing flows of rural migrants, there will be higher demand for infrastructure, basic services and jobs. This could take place in parallel to an increase in the share of climate-vulnerable populations living in intermediary cities and disruptions of food supplies. Moreover, the institutional, financial and governance gaps characterising intermediary cities will be exacerbated by the effects of climate change. However, climate change may in some cases hinder migration towards intermediary cities. Indeed, Peri and Sasahara (2019[27]) find that rising temperatures slow the rates of rural-to-urban transition in poor countries, with climate change trapping rural populations in poverty through lower income from agriculture, thus reducing their capacity to migrate to urban areas (Peri and Sasahara, 2019[27]).
Intermediary cities, climate change and food systems
Climate change is becoming a major threat to food systems, particularly in developing economies. Because of the interconnectedness and complexity of food systems, disruptions caused by climate change may have exponential consequences that will impact not just other elements along the chain, but potentially the livelihoods of those living along the rural-urban continuum. Intermediary cities are placed at the core of food systems. Although they will be exposed to the indirect effects of climate change, intermediary cities can also function as key agents for development. Since the existing vulnerabilities of intermediary cities are likely to have a ripple effect on their vulnerable populations and their links with surrounding rural areas, adopting mitigation and adaptation strategies should be at the top of local agendas.
This section starts by conceptualising food systems and their interlinkages, highlighting the role of intermediary cities. It then reviews the effects of climate change on food systems and how this will impact intermediary cities. Finally, it provides insights into measures that could be implemented in urban areas to deal with this issue.
Conceptualising food systems
Food systems refer to the complex networks of actors and activities that operate along food supply chains. These include all the core activities that take place along food value chains, including production, aggregation, processing, distribution, consumption and disposal (Nguyen H, 2018[15]; IFPRI, 2020[28]; Reardon and Zilberman, 2018[7]). Food systems also embed wider subsystems or sectors (including energy, water, financial services, trade, health, etc.) that serve as feeders to activities along the supply chain. These subsectors include input-supply systems in the upstream segments of the chain (such as supply of fertilisers, seed, farming equipment, etc.), supply systems in post-farm-gate systems (financing and credit systems, transportation, road infrastructure, etc.), and feeders in the downstream activities of food systems (Reardon and Zilberman, 2018[7]). These can include governance and policies, infrastructure and the environmental and socio-economic dynamics underpinning the operation of activities along the downstream segments of food systems (Reardon and Zilberman, 2018[7]; Dury et al., 2019[10]; IFPRI, 2020[28]; Tendall et al., 2015[29]). The food economy can be further classified and measured through national accounts systems (Box 3.2). Most importantly, all these segments and subsystems are interlinked, and changes in one segment will automatically induce changes across the value chains (Reardon and Zilberman, 2018, p. 3[7]).
Box 3.2. Categorising the food economy
National accounts often categorise food-economy activities across three sectors: primary, secondary and tertiary. The primary sector entails production activities within the agricultural, mining, forestry or fisheries sectors; the secondary sector includes all processing and manufacturing of food intended for human consumption; and the tertiary sector encompasses post-production activities and logistics including transportation, distribution, wholesale and retail sales, and restaurant and street-food activities, among others (Allen et al., 2018[9]).
Tschirley et al. (2016[30]) further subcategorise activities and labour allocation within food systems across four segments: 1) food agriculture, entailing all activities within the primary sector; 2) food processing, including all activities the within the secondary sector; 3) food marketing (transport, logistics, wholesale, retailing) within the tertiary sector; and 4) food-away-from-home (restaurants, street food, catering services), also within the tertiary sector (Allen et al., 2018[9]; Tschirley et al., 2016[30]).
Food systems in developing countries are transforming rapidly
Food systems account for a significant share of the economy of developing countries and will continue to grow in the coming years. Over the past 30 to 40 years, the food economy has grown exponentially – eight fold in regions such as Africa, where it is expected to grow another six fold within the next four decades (Badiane and Makombe, 2015[31]; Haggblade, 2011[32]). Moreover, the food economy accounts for the lion’s share of employment across developing countries, involving more than 2 billion workers globally (Dury et al., 2019[10]). In West Africa, for example, the food economy is the biggest source of employment, accounting for 66% of total employment, with a total of 82 million jobs (Allen et al., 2018[9]). Within the food economy, the agriculture sector remains the predominant source of employment, accounting for 70% of total employment in low–income countries, and 60% of total employment in Sub-Saharan Africa; as such, it plays a fundamental role in poverty reduction (World Bank, 2017[33]). Moreover, local food systems are an integral aspect of food security. Indeed, in Africa and Asia, 90% of urban dwellers rely on local food systems for the food they eat, and local food systems provide 50% to 80% of the diets of rural households (Reardon and Zilberman, 2018[7]; Reardon et al., 2016[34]).
In Western and Central Africa, agriculture accounts for the biggest share of the food economy (and of the total economy), according to OECD estimates. In countries like Chad and Niger, agriculture represents around 80% of the total economy (Figure 3.2). Nonetheless, activities in the mid- and downstream segments of food systems also play an increasingly important role (Dury et al., 2019[10]). In some developing regions, they account for a significant share of the manufacturing and service sectors (World Bank, 2017[33]).
Food systems are a key source of livelihood for women and other marginalised groups such as youth, although with different employment prospects across rural and urban areas. For instance, many rural youths in developing countries are engaged in the primary sector or in the production segments of agricultural value chains (OECD, 2018[35]). Overall, the agriculture sector accounts for 37% of rural youth employment in developing countries; in countries like Uganda and Tanzania, it can exceed 90% (Figure 3.3, top).
Urban areas, in contrast, provide differing prospects for youth employment. Urban areas in higher-income countries tend to see larger shares of youth employed in the downstream segments of food systems, with food processing as the main activity. In lower-income countries, food marketing is the dominant activity in urban areas (Figure 3.3, bottom) (OECD, 2021[13]). This indicates that as developing countries become richer, urban areas (especially intermediary cities) will be increasingly important sources of diversified employment in the food economy, especially for the marginalised. Indeed, small and medium-sized cities and towns are receiving increasing recognition for their role in employment and income diversification, within and outside of the food economy (Hussein and Suttie, 2016[36]; Agergaard et al., 2019[37]; Tacoli, 2017[38]; IFAD, 2010[39]). This is because they function as key nodes that consolidate off-farm activities (including the manufacturing sector) (Tacoli, 2017[38]), as will be further discussed below.
The growth of the food economy in the past decades has enabled better access to wage employment, especially for women. In West Africa, the food economy accounts for 68% of total female employment in the region (Allen et al., 2018[9]). Downstream sections of food systems, although still relatively small, employ a higher number of young women than young men (Allen et al., 2018[9]). This is the case in the Niayes region of Senegal, where the food economy in 2020 accounted for 77% of female employment, of whom 63% were engaged in the marketing, 7% in processing and 25% in agriculture (OECD/SWAC, 2020[40]). The informal sector is also important in the creation and expansion of employment opportunities for women and youth (Box 3.3).
Box 3.3. The critical role of informal networks in food systems of developing countries
Informal networks play an integral role in food systems along the value chains linking rural and urban areas in developing economies. A large share of food activities are conducted within the informal sector, from agricultural production all across the value chain, including retail sectors (Vorley et al., 2020[41]; Allen and Heinrigs, 2016[42]). Informal networks are key providers of food and nutrition security. They provide livelihoods, especially for poor and marginalised groups (including women and rural and urban youth), and play an important role in meeting the growing demand for food in urban areas.
The accessibility and affordability of informal food systems makes them an important source of food security for low-income rural and urban households (Vorley et al., 2020[41]). Informal networks are integral to the “food away from home” segments of the food economy, as street-food vendors are key suppliers to (low income) urban dwellers, who are highly dependent on “ready to eat” food (Allen and Heinrigs, 2016[42]). In Southeast Asia, for instance, a large share of the urban poor, including consumers as well as traders, rely heavily on informal food sectors (Tacoli and Vorley, 2016[43]). In Southern Africa, a survey of 6 000 households conducted across 11 cities found that 70% of households rely on the informal food economy for their regular food purchases (Frayne B et al., 2010[44]).
The last 50 years have seen a transformation of food systems, particularly in developing countries. Some of the key determinants include changes in diets, urbanisation and the modernisation of food retail markets, including the use of new technology (Berdegué et al., 2014[3]; Reardon and Zilberman, 2018[7]). These interlinked determinants have primarily transformed the midstream (processing, wholesaling, etc.) and downstream (retail, fast food, etc.) segments of food systems. Changes in food systems have also been underpinned by “meta-conditioners” that facilitate the transformation process. These are national and international factors including: population and income growth; widespread liberalisation and privatisation policies; and increased investment in infrastructure across developing regions. These factors have led to increased participation of the private sector, changes in diets, higher labour-market participation, improved access to markets in urban areas and the development of supply chains (Reardon and Zilberman, 2018[7]). The transformation of food systems in some regions has implied the transformation of productive activities as well due to the way these activities are co‑ordinated (Box 3.4).
Box 3.4. Transitioning from Food System 1.0 to Food System 2.0
With food systems evolving significantly over the years, Jennings et al. (2015[45]) have classified the transformations into Food System 1.0 and Food System 2.0. The defining characteristic of Food System 1.0 is the prevalence of informal actors, who work and conduct business in a generally localised and decentralised way. Most of the wastage produced in this paradigm is by production and aggregation activities. Smallholder and subsistence farmers are the main actors involved in production activities.
In contrast, Food System 2.0 is formal and centralised, with fewer actors involved than in Food System 1.0. Sophisticated global supply chains and increased efficiency help keep prices down and wastage low in the production and aggregation phase. The consumer produces most of the wastage (Jennings et al., 2015[45]).
Although developing countries are often characterised by Food System 1.0, they have been undergoing large transformations in recent decades, transitioning from fragmented and short supply chains to more complex supply chains that are growing in length and volume (Reardon and Zilberman, 2018[7]; Tschirley et al., 2015[46]).
Fast urbanisation is leading to the rapid expansion of supply chains, prompting large transformations in food systems. In the last 30 years, rural-urban supply chains have grown by 600% to 800% in Africa (Haggblade, 2011[32]) and by 1 000% in Southeast Asia (Reardon and Timmer, 2014[47]). This is underpinned by the fact that urban households tend to have higher incomes than their rural counterparts: food expenditure makes for a lower share of the household’s budget, with higher food consumption per capita (Reardon and Zilberman, 2018[7]; Badiane and Makombe, 2015[31]). This is the case across emerging regions. Even in the least urbanised countries in Africa, cities dominate the food market. For instance, while only 25% of the population live in cities in Eastern and Southern Africa, urban areas account for 48% of food consumption (Badiane and Makombe, 2015[31]; Tschirley et al., 2015[46]). In West Africa, where the urban population grew from 5 million in 1950 to 133 million in 2010, spending on food accounts for 46% of the total expenditure of urban households, compared with 60% of the total expenditure of rural households (Allen and Heinrigs, 2016[42]). In Asian markets, urban food demand accounts for more than 65% of domestic food and depends on rural-urban food-supply chains (Reardon and Timmer, 2014[47]).
Changes in trade dynamics, consumer demand and urbanisation will contribute to structural transformation in low- and middle-income countries, and will continue to make food systems key sources of employment across developing economies. For example, between 2010 and 2025, transformation of food systems across six African countries (Ethiopia, Malawi, Mozambique, Tanzania, Uganda and Zambia) is projected to be the biggest source of employment (World Bank, 2017[33]). In the countries studied, employment in farming was expected to decline from 75% to 61%, while employment in the midstream and downstream segments will rise from 8% to 12% during the same period (World Bank, 2017[33]). Moreover, a study conducted by the OECD (2021[13]) highlights that provided adequate policies are implemented, employment in the food economy is expected to rise across 11 economies1 in Sub-Saharan Africa from 2019 to 2030, with a 17% increase in the upstream (agricultural) segment and an increase in employment in the mid- and downstream segments from 21 million to 29 million. However, the relation of employment in the food economy to total employment will not change dramatically over the period, remaining at nearly 60% of total employment in the region (OECD, 2021[13]). In Thailand and Vietnam, employment in the food economy is expected to remain relatively stagnant, with a slight increase in Vietnam. Employment will decline in the agricultural sector and will increase slightly in the downstream segments of food systems, by 3% in Thailand and 4% in Viet Nam (Figure 3.4) (OECD, 2021[13]).
Intermediary cities are at the heart of food systems
Activities within food systems are dynamic and operate in continuous bi-directional movements across the rural-urban interface. Although it is easy to assume that food systems operate in only one direction, with rural areas as the location of production and urban areas as consumption centres, there are complex feedback loops that link these territories (see Figure 3.1 above). Often, rural areas source their agricultural inputs (such as fertilisers, pesticides, etc.) from production centres located in small and medium-sized cities (Berdegué et al., 2014[3]). Additionally, a large share of rural households are net food buyers, and rely on urban markets for their consumption of processed foods (Reardon and Zilberman, 2018[7]; IIED, 2015[48]). For example, in rural areas of Bangladesh, Indonesia, Nepal and Viet Nam, consumption of processed food (produced in urban or peri-urban areas) accounts for 59% of total rural consumption (Tacoli and Vorley, 2016[43]). In parallel, people living in urban areas can be involved in upstream activities. This is the case in West Africa, where urban dwellers may conduct agricultural activities in surrounding peri-urban or rural areas (Allen and Heinrigs, 2016[42]).
The intermediation role of intermediary cities places them at the heart of food systems. First, their closer linkages to rural areas enable rural households to have better access to local markets (FAO, 2017[5]). For instance, findings from Ethiopia highlight stronger agricultural linkages between rural areas and small cities than between rural areas and larger/metropolitan cities (Dorosh et al., 2012[49]). Second, unlike capital or metropolitan cities, intermediary cities are less dependent on internationally or nationally imported food, and tend to rely more on food provided from surrounding rural areas (Berdegué et al., 2014[3]). This further supports the transformation of local value chains. Third, the intermediation role of these cities acts as an entry point to agri-value chains for smallholder farmers, who are often excluded from formal supply chains (Berdegué et al., 2014[3]; Tacoli and Agergaard, 2017[11]; Allen and Heinrigs, 2016[42]). Box 3.5 highlights the strategic role of intermediary cities in the transformation of food systems.
Box 3.5. Intermediary cities are a key factor in the transformation of food systems
The current transformation of food systems in developing countries is taking place along the rural-urban interface. Reardon and Timmer (2014[47]) situate this process in Asia across three zones: 1) “dynamic commercial zones”, located within 8-10 hours of urban catchment areas; 2) “intermediary zones”, including urban centres, that function as “economic pull” for the supply of goods from rural areas; and 3) “hinterland traditional, semi-subsistence zones”, or remote rural areas with weak linkages to urban centres and markets (Reardon and Timmer, 2014[47]).
Intermediary cities, which are located in the “intermediary zones”, are key in the transformation process. The evolution or transformation of food systems (partially due to rising urbanisation) is creating stronger and closer linkages between producers and consumers distributed across rural and urban territories. The development of networks of small towns and medium-sized urban centres has been an integral aspect of food system transformation. These urban centres serve as “nodes for the spatial organisation of trade and markets”, and play a fundamental role in facilitating the integration of rural areas into market economies (Allen and Heinrigs, 2016, p. 5[42]). They host and link actors and activities along value chains, especially in the post-farm-gate or intermediate segments of the supply chain (Reardon and Timmer, 2014[47]). Urban markets have become particularly important for an increase in high-value and non-grain products, which are now transported along rural-urban supply chains (IFAD, 2016[50]).
It is important to note, however, that sustainable and inclusive transformation of food systems goes hand in hand with rural transformation. Indeed, intermediary cities and their urban consumers are highly reliant on rural areas for food, clean water, raw materials and environmental services. There is thus a growing interdependence as well as a blurring of boundaries between rural and urban areas (and people living in these territories) (Hussein and Suttie, 2016[36]), and this is an important factor in the ongoing transformation of food systems.
Their stronger linkages with smallholder farmers in surrounding rural areas place intermediary cities at the core of food systems. Smallholder farmers2 account for more than a third (35%) of global food production (Lowder, Sánchez and Bertini, 2021[51]). In regions such as Sub-Saharan Africa, South Asia and East Asia and the Pacific, smallholder farmers account for 80% of the farms, representing 30%-40% of the total share of land (Lowder, Sánchez and Bertini, 2021[51]). For this reason, intermediary cities can highly benefit from their stronger linkages with food producers. These closer links are also key determinants of rural off-farm employment. Off-farm activities within agri-food systems (including food processing) tend to be established closer to urban centres (Berdegué et al., 2014[3]; Hussein and Suttie, 2016[36]). The development of road and service infrastructure (such as electricity) across these urban centres has enhanced their role as key locations for off-farm activities (Berdegué et al., 2014[3]; Badiane and Makombe, 2015[31]).
While urbanisation has had a transformative effect on food systems, it can also have detrimental effects if it is not appropriately planned. Unplanned urbanisation and urban sprawl can reduce the amount of land available for agricultural production and decrease the productivity of food systems (Box 3.6).
Box 3.6. The effects of urban sprawl on surrounding productive land
Urban sprawl can have negative implications for agriculture, and can consequently lead to issues of food security. This is a rising issue across various regions.
A study by Li (2018[52]) found that the metropolitan area of Buenos Aires (Argentina) had doubled over 20 years, expanding from 937.16 km2 in June 1985 to 1 835.47 km2 in July 2015, with 30.28% of the new urban land coming from existing cropland. Urban use of land has also increased rapidly in suburban areas of Jakarta (Indonesia), where the built‐up area increased by 21.56% between 2001 and 2009, resulting in a decrease in forest area of 8.51% and a decrease in farmland of 5.78% (Nagasawa et al., 2015[53]). Similarly, the city of Bogotá (Colombia) expanded almost four fold over 29 years, from 151.2 km2 in 1985 to 567.5 km2 in 2014, and this expansion occurred at the expense of forests and farmland (Romero et al., 2020[54]) .This transformation impacts agricultural production and food security in the region, as the lands around Bogotá are among the most productive in the Andean region (Romero et al., 2020[54]).
In a study of urban sprawl in India, meanwhile, Pandey and Seto (2015[55]) note that urban expansion and the loss of agricultural land has increased steadily since 2006. They find that agricultural land loss has occurred near smaller cities more than large urban centres that urban conversion of agricultural land is concentrated in a few districts and states with high rates of economic growth, and that agricultural land loss is predominantly in states with higher agricultural land suitability. A separate study of urban sprawl in the Delhi periphery concludes that agricultural land loss could cause economic deprivation and increase poverty and socio-economic vulnerability (Das, 2017[56]; Abu Hatab et al., 2019[57]).
Climate change is disrupting food systems
As we enter the last decade of the Sustainable Development Goals (SDGs), the sustainability of global food systems is more important than ever. Building sustainable and resilient food systems is now being recognised in international policy arenas. For instance, the United Nations held its Food Systems Summit in 2021 with the aim of transforming global food systems and setting them on a path for the achievement of the SDGs. Climate change appeared in one of the five Action Tracks of the summit’s agenda. There was recognition of the need to act on climate not only by reducing emissions from food systems, but also by promoting the protection of ecosystems and reducing food loss and energy use while ensuring nutritious diets (Caprile, 2021[58]). Similarly, 190 countries participating in the 26th Conference of Parties to the UN Framework Convention on Climate Change recognised the need to address agriculture and its link to climate change, with particular focus on the need to improve soil and nutrient management practices for building sustainable and resilient food production systems and contributing to global food security (UNFCCC, 2021[59]). While this reflects progress in addressing the climate vulnerability of global food supply systems, there is still a need to recognise and better articulate the vulnerability to climate change of the entire food supply chain in order to ensure resilient supply chains and food security.
Climate change will increasingly affect the dynamics of food systems. It acts as a “threat multiplier” and poses serious risks to the functioning and sustainability of food systems (IIED and Hivos, 2020[60]). Food systems are comprised of a network of complex and interdependent segments, which are critical for food security and the livelihoods of both rural and urban dwellers. Climate-induced threats in one segment can lead to a series of disruptions along the full supply chain (Reardon and Zilberman, 2018[7]). This can ultimately affect the availability, access, utilisation and long-term stability of food supplies (Reardon and Zilberman, 2018[7]). Figure 3.5 illustrates climate-induced changes along food supply chains and their socio-economic outcomes in intermediary cities.
Food systems will continue to be under pressure to feed the growing global population and to adapt to changes in food demand patterns. Estimates suggest that food supply will have to increase by 50% by 2050 to address these demands (FAO et al., 2018[1]).
Climate change will particularly affect regions and marginalised populations with low adaptive capacity. Poor and marginalised communities in developing countries are particularly vulnerable, as they are disproportionally exposed to food insecurity and loss of livelihood (Vermeulen et al, 2012[2]). Indeed, declining agricultural outputs will lead to a rise in food prices, causing significant strains on populations facing food insecurity.
Climate change will affect food systems by disrupting their production capacity and sustainability. This will result from a combination of issues, including a decrease in productivity in the primary sector and changes in the quality and types of crops. To assess the effects of climate change that will ultimately impact intermediary cities, the following section considers food systems by production (upstream activities) and post-production segments (midstream and downstream activities).
Climate change harms agricultural productivity
Climate change will lead to a decrease in crop yields in some regions. Crop yields are highly vulnerable to changes in temperature and water availability. Despite large regional variations, an overall increase in temperatures and reduced water availability are expected to lead to a decline in crop yields, especially across rain-fed farming systems (Myers et al., 2017[61]). Lizumi et al. (2018[62]) estimate the effects of climate change on the global average yields of maize, rice, wheat and soybeans for 1981-2010 (relative to the pre-industrial climate). Their findings indicate that climate change has had no significant impact on rice, but that it will significantly impact the yields of maize, wheat and soybeans. They estimate that, over the period, climate change decreased the global mean yield of maize by 4.1%, wheat by 1.8% and soybeans by 4.5%, relative to the pre-industrial climate. Estimates project that the decrease in yields will be more acute in South Asia, Sub-Saharan Africa and Southeast Asia (World Bank, 2010[63]). The reduction in crop yields is expected lead to significant economic losses. For instance, between 2005 and 2009, global yield losses amounted to USD 22.3 billion for maize, USD 6.5 billion for soybeans, USD 0.8 billion for rice and USD 13.6 billion for wheat (Lizumi et al., 2018[62]). However, losses in yields will vary considerably by region, CO2 concentration and land fertility (Mbow et al., 2019[23]). For instance, temperate regions are expected to observe an increase in yields up to 2050, while tropical areas are expected to experience a yield decrease. Beyond 2050, all regions are expected to experience yield loss (Vermeulen et al, 2012[2]).
Moreover, rising temperatures will lead to a loss of working hours in agriculture due to heat stress. People working in agriculture will be the most affected by rising temperatures, especially those in countries with deficient working conditions. The International Labour Organization’s most conservative estimates suggest that a temperature rise of 1.5ºC would translate into a loss of 2.2% of total working hours worldwide and a drop in the world’s GDP of USD 2 400 billion in 2030. In these estimates, agriculture alone accounts for 60% of the loss (ILO, 2019[64]). By 2030, Western Africa, Southeast Asia and Southern Asia will be the regions experiencing the largest losses in working hours due to heat stress (Figure 3.6).
Climate change affects the predictability and quality of agricultural crops
Climate change can also cause unpredicted changes in the types of crops that can be produced under new climatic and unpredicted seasonal changes (Mbow et al., 2019[23]). Sudden extreme weather conditions cause loss of harvest and infrastructure, and they particularly impact producers with limited or no safety nets. For example, in the context of India, understanding the arrival of the monsoons plays a critical role in planning harvests. Climate change can cause variations in the arrival of the monsoons and cause floods and droughts, depending on the region (Loo, Billa and Singh, 2015[65]).
Beyond its impact on production, climate change can also change the nutritional composition of crops. The increase of pollutants in the atmosphere, such as ozone, black carbon, methane and CO2, can damage crops and modify their nutritional level (especially crops such as wheat, rice, soybeans and green beans) (Mbow et al., 2019[23]). Increases in global temperatures are expected raise the concentration of these pollutants; it is estimated that an increase in ozone concentration can lead to crop damage of up to 20% by 2050 (Mbow et al., 2019[23]; Chuwah et al., 2015[66]). Climate change is known to increase the concentration of ozone in the atmosphere (EUC, 2010[67]), while ozone, a key component of GHG emissions, contributes to climate change by trapping heat in the atmosphere.
Climate shocks will also affect livestock and fisheries
Climate change will also cause considerable losses in other activities in the primary sector. Climate-induced changes such as rising temperatures and decreasing water availability will affect livestock production through their effect on feed supplies and spread of diseases, and will ultimately cause higher livestock mortality (Vermeulen et al, 2012[2]; Mbow et al., 2019[23]). Climate change may also lead to a large-scale redistribution of the catch potential of global fisheries, with an average increase of 30%-70% in high-latitude regions and a drop of up to 40% in the tropics (Cheung et al., 2010[68]). Although aquaculture was set to become a substitute or solution for declining global fisheries (Naylor et al., 2000[69]), the sector is particularly vulnerable to climate-induced threats, such as shortages of water and infrastructure, which that cause production losses, the spread of disease, development of toxic algae and parasites, thereby reducing farming capacities (Barange et al., 2018[70]).
Climate-induced damage will disrupt post-production along food supply chains
Post-production activities, operating along the midstream and downstream segments of supply chains, are also vulnerable to the long- and short-term effects of climate change.
Damage to essential infrastructure can cause losses along food system supply chains. Fast-onset climate effects (floods, hurricanes, typhoons and other disasters) can damage and disrupt power supply for food storage facilities and road and transportation infrastructure (FAO, 2008[71]). These risks can also affect post-production sub-segments along the supply chain, including damage to irrigation and drainage systems and to the power supply needed for production, processing and storage of food (Reardon and Zilberman, 2018[7]; FAO, 2008[71]). The extent of climate-induced damage or disruption is determined by a series of factors underpinning the operations of segments along the value chains (Box 3.7).
Box 3.7. Factors determining the vulnerability of food systems to fast-onset climate events
The vulnerability of food systems to short-term (or fast-onset) climate events depends on a series climate-risk “hotspots” along the value chains. These hotspots can be present along the various segments of food value chains as well as their subsegments (such as input systems for farming, irrigation, infrastructure, etc.). Reardon and Zilberman (2018[7]) outline characteristics that shape the vulnerability of food systems to climate risks. These include:
type and resilience of the infrastructure that supports production systems, including drainage, irrigation and flood-control systems, which are crucial for controlling the risks to which supply chains are exposed.
geographic distances along the supply chains, such as the distance between points of assemblage, processing, distributing, etc. Longer supply chains heighten the vulnerability of food systems, especially in circumstances with low urban food-supply diversification.
perishability of products. Highly perishable products require fast delivery and adequate storage systems, leaving them vulnerable to climate shocks.
extent of physical capital intensity along food-system segments. The use of robust physical capital (including irrigation systems, storage, transportation systems, etc.) can help reduce exposure to climate risks. Moving from traditional to modern food supply chains often increases the capital-to-labour ratio, with an overall decrease in climate vulnerabilities.
location and asset specificity in intermediation production processes. Lack of interchangeable locations for crop production and logistics at intermediary segments can lock in supply chains to climate shocks.
concentration of supply chains. Supply chains dominated by a few large firms, instead of larger numbers of small firms, can expose food systems to disruptions caused by climate change.
variations in exposure to climate risks over time. This can be over one or multiple locations which may vary in their exposure to climate risks, and can exacerbate food system vulnerabilities.
Source: Reardon and Zilberman (2018[7]).
Climate change can also cause changes in the procurement patterns of supply chains. Long-term (or slow-onset) climate effects – changing temperatures, droughts, desertification, sea-level rise, ocean acidification – may lead to permanent changes and can change the procurement patterns of supply chains. Rising temperatures can increase the need for cooling and storage systems to maintain food quality, which implies more demand for energy (FAO, 2008[71]). Hotter weather can also cause food safety risks and wastage, especially along the supply chains of perishable goods (Vermeulen et al, 2012[2]). Changes in rainfall or droughts can cause shortages of water for food processing, and can create challenges for the transportation of goods. Other events, such as flooding, can cause pollution of water used for processing purposes (FAO, 2008[71]). Some of the repercussions of long-term climate effects can lead to significant long-term or permanent changes in the production segments and established supply-chain networks of food systems. For example, it is estimated that some areas of Nicaragua will lose 40%-60% of their suitability to grow coffee by 2050. This will affect the whole range of intermediaries who must adapt and change the connectivity of their supply chains (Laderach et al., 2011[72]). it could eventually lead to shifts in other economic activities because of low productivity and reduced profitability.
Climate-induced changes in food supply chains can cause an increase in the cost of public and private investment. Slow-onset climate events will lead to higher costs in terms of investment in alternative processing, storage or retail facilities; could cause businesses to decide to relocate to less risky areas; and could impose changes in logistics and procurement strategies. Regions or countries with inadequate or low-quality infrastructure will face large losses or rising maintenance costs (Vermeulen et al, 2012[2]). As such, responses to slow-onset climate events may lead to the adoption of innovations, migration, changes in trade and land-use strategies, and overall permanent changes in the midstream and downstream segments of food systems (Reardon and Zilberman, 2018[7]).
Food systems today are under larger pressure than ever due to the combination of climate change, increasing demand for food and the effects of COVID-19. This has been exacerbated by the Russia-Ukraine war, which has caused high price volatility in food staples including wheat, maize, soybeans and cotton (Rice et al., 2022[73]). Urgent actions to transform the current global food system are needed to cope with the unprecedented pressure on food systems and the disruptions in global value chains.
Developing countries will be disproportionally affected by the rising pressure on global and local food systems. Box 3.8 highlights some of the main challenges that COVID-19 has posed for food systems, especially in developing countries.
Box 3.8. COVID-19 has put additional strains on global food systems
COVID-19 has caused multidimensional challenges to global food systems and their governance. It has also set back progress towards the attainment of the SDGs, as many people have been pushed back into poverty (IFPRI, 2021[74]). Indeed, in 2020, the UN World Food Programme projected that the number of people facing hunger and food insecurity had nearly doubled over a year, rising from 135 million in 2019 to 265 million in 2020, mainly in low- and middle-income countries (WFP, 2020[75]). This puts national and local governments and global food systems under additional pressure as they seek to address the triple challenges of increasing hunger and malnutrition, climate-change-induced stress and an increase in global demand for food (IFPRI, 2021[74]; OECD, 2020[76]).
COVID-19 has caused losses of income and livelihood, disruption of food supply chains and decreases in nutrition and food security, as well as exposing and magnifying socio-economic inequalities across and within countries (IFPRI, 2021[74]). A survey conducted by the FAO (2020[77]) of 860 respondents, including municipalities, national governments and academic and non-governmental institutions, found that food supply chains had been negatively affected by containment measures implemented to reduce the spread of the virus, such as restriction of movements of goods, services and persons; closure of restaurants and food services; restrictions on the use of public transportation and public space for food markets; and labour shortages. Moreover, panic buying of large volumes of food by some urban dwellers further contributed to food insecurity and a rise in food prices (FAO, 2020[77]).
The spatial effects of COVID-19 are complex. Urban dwellers were most burdened overall by loss of income, shrinking economies and lockdown restrictions. Yet despite a buffer of protection for rural agricultural households, overall poverty increased both in rural and urban areas. This reflects the greater vulnerability of rural households of falling into poverty despite the lesser direct impact of the pandemic. Disruptions in flows of remittances across rural and urban areas, as well as internationally, also contributed to the increase in poverty in rural areas (IFPRI, 2021[74]). At the same time, geography and density played key roles in the ways urban areas were affected by interruptions in food supply chains. Larger and dense cities with more than 500 000 inhabitants were the most affected, while smaller towns and villages, with shorter supply chains and better proximity to production sites, were the least affected (FAO, 2020[77]).
Local governments played the main role in implementing COVID-19 restrictions and relief programmes. They faced enormous challenges in addressing the economic and social crisis created by the pandemic. More than 70% of municipalities surveyed by the FAO implemented COVID-19 measures and relief programmes with no or insufficient additional funding from national governments (FAO, 2020[77]).
With shocks caused by pandemics and climate disasters likely to be more frequent in future, it is now more urgent than ever to build food systems that are resilient, equitable and sustainable. The recovery process from the COVID-19 crisis should be taken as an opportunity to transform food systems globally (IFPRI, 2021[74]; OECD, 2020[76]). This entails building resilient food systems that anticipate and mitigate the effects of external shocks and that are flexible enough to adapt to changing global conditions (IFPRI, 2021[74]; OECD, 2020[76]). Improved public spending on health and other social protection systems, especially for vulnerable populations, is a key aspect of building resilience (IFPRI, 2021[74]).
Intermediary cities face ripple effects from the impact of climate on food systems
Intermediary cities are key actors in the complex dynamics between climate change and food systems. By virtue of their socio-economic and geographic closeness to rural areas, and the important role they play in the supply chains of food systems, intermediary cities face a series of indirect effects from climate change, which will cause large disruptions in the food economy and present a series of socio-economic challenges.
However, the effects of climate change are not linear. Instead, climate change will have ripple effects on the networks of activities connecting food systems, eventually affecting intermediary cities:
Climate change can cause losses of livelihood and income for a large share of inhabitants of intermediary cities. Intermediary cities in low- and middle-income countries are heavily dependent on the primary sector (including agriculture, fishing, mining, etc.) for their local economy (Berdegué et al., 2014[3]; Roberts et al., 2016[24]) and are closely linked to the economies of their surrounding rural areas (Reardon et al., 2016[34]). These sectors are the source of livelihood for a large share of urban dwellers. In West Africa, for example, agriculture accounts for 34% of total employment in the region’s urban food economy, and a large share of producers are in small and medium-sized cities (Allen et al., 2018[9]). Moreover, as highlighted above, post-production segments of food systems, including processing and “away-from-home” segments, are often located in intermediary cities, and act as a source of employment for a significant share of urban dwellers. The effects of climate change on food systems, such as drops in agricultural yield, livestock and fisheries, as well as reduced productivity due to heat stress or unsuitable crops, cause disruptions along supply chains and could mean large losses of livelihood for urban dwellers in intermediary cities.
Climate-change effects on food systems can cause urban food insecurity. Intermediary cities and small towns highly depend on surrounding rural areas for the procurement of food for urban dwellers (Reardon et al., 2016[34]). Climate-induced threats cause unpredicted changes in the availability and suitability of crops used for consumption and pose major risks to post-farm-gate infrastructure. These threats can cause large losses in production value as well as food wastage, and can interrupt the food supply chain linkages between intermediary cities and their surrounding areas (Forster et al., 2015[78]). Such climate-induced disruptions can reduce food availability (Satterthwaite et al., 2010[79]), as well as causing increases and/or severe fluctuations in food prices in urban food markets (Tacoli et al., 2013[80]). This puts at stake both the availability and affordability of basic food and its nutritional quality (Mbow et al., 2019[23]).
Climate-change effects on food systems can reduce the ability of urban dwellers to diversify their incomes through their links to rural areas. As highlighted above, urban dwellers in intermediary cities conduct their livelihoods in both urban and rural areas, and this affects their food security. For example, findings from two intermediary cities in Uganda (Mbale and Mbarra) highlight that food-secure households tend to have strong links to surrounding areas in terms of livelihoods and social networks. Thus, the most food-secure urban households were those that were employed within urban areas in addition to having active rural lives and livelihoods (Mackay, 2019[81]).
The impact of climate change on in intermediary cities will be more acute for vulnerable populations (women, youth, migrants), further aggravating inequalities. Losses of livelihood would disproportionally affect marginalised groups such as urban women and youth, as they tend to be overrepresented in sectors such as food-away-from-home or market stalls (OECD/SWAC, 2020[82]). Sudden price shocks can have a particularly negative impact on poor or marginalised urban dwellers who rely on irregular incomes and are unable to adjust to sudden price fluctuations (Tacoli et al., 2013[80]; Von Braun and Tadesse, 2012[83]). These compounded effects, along with a lower food supply, cause food inaccessibility and increase food insecurity and malnutrition among the most vulnerable groups (Tacoli et al., 2013[80]; Tefft et al., 2017[84]).
Intermediary cities, climate change and internal migration
Climate change is expected to disrupt rural livelihoods severely and trigger internal migration. Natural disasters or land degradation and water scarcity can prompt rural populations to migrate to intermediary cities, which are closer, culturally more similar and arguably less costly than large urban centres. Understanding the factors influencing migration decisions and the vulnerabilities of the migrants upon arrival is crucial. Intermediary cities can actively work to improve the well-being of rural migrants through local policies, which are likely to have positive spillovers on the cities as well.
The section starts by considering internal migration as a global phenomenon and its role in the growth of intermediary cities. It then assesses migration decisions and coping strategies in the event of shocks. It next focuses on the effects on migration of fast-onset and slow-onset climate effects and the indirect effects these could have on intermediary cities. Finally, a non-comprehensive list of local measures is presented to spark debate on the role of intermediary cities as agents of change.
Conceptualising internal migration
Internal migration is a key factor shaping countries’ development processes. At the beginning of the millennium, there were an estimated 740 million internal migrants, three times the estimated number of international migrants (FAO et al., 2018[1]; UNDP, 2009[85]; Rigaud et al., 2018[86]). According to the UN Development Programme (UNDP) (2009[85]), internal migrants represented 10% of the global population, while international migrants represented 3% (FAO et al., 2018[1]; UNDP, 2009[85]). However, it is important to note that these internal migration estimates by the UNDP (2009[85]) were rather conservative. While methodologies have improved in recent years (Bell et al., 2015[87]), large difficulties remain in obtaining consistent definition and estimations of global internal migration (IOM, 2008[88]).
The interaction of international migration with climate change, while an important factor, is outside the scope of this report, for two reasons: first because this report focuses on the linkages between a country’s rural and urban areas, and second because international migration has received much more attention in the literature than internal migration, despite being of smaller magnitude. Nevertheless, international migration also has an important role in shaping urbanisation in developing countries and in food systems. For instance, Venezuelan migrants have been employed in Colombian coffee plantations that were facing low labour supply, as rural youth in Colombia switched to different economic sectors, and internal migration to work on plantations had slowed down (Federacion Nacional de Cafeteros de Colombia, 2017[89]).
Patterns of internal migration vary across regions and even by gender. These patterns can be categorised as rural-to-rural, rural-to-urban, urban-to-rural and urban-to-urban migration. The FAO (2019[90]) has estimated the prevalence of these different types of movements within and across regions in various developing economies. Figure 3.7 shows the dynamics of these internal movements. Although the dynamics vary substantially among countries within one region, some patterns stand out. For instance, in regions such as Sub-Saharan Africa and South Asia, more people tend to migrate across rural areas than to urban centres, and especially women (in Burkina Faso, 85% of female migrants settle in a rural region). In Latin America and the Caribbean, in contrast, most of the migration is to urban areas, and these patterns are similar among men and women.
Despite differences in destination, an important share of internal migration originates from rural areas. Even in regions with high urbanisation, a sizeable share of migrants leave from rural areas. This is the case in Latin America and the Caribbean, which has an average urbanisation rate of 81% as of 2019 (UNDESA, 2018[91]) and where a high proportion of internal migrants nonetheless have a rural origin. In Haiti and the Dominican Republic, for instance, 58% of the movements of men originate in rural areas, while for Haitian women this statistic rises to 62%.
Climate change is a key driver of international or cross-border migration, and intermediary cities can be affected. Climate-driven international migration particularly affects countries in the Global South, such as Bangladesh, Syria, Sudan and India, among others (Wesselbaum et al., 2021[92]). While more research and data is needed for better understanding of the links between climate change and international migration, internal migration from rural to urban areas is often the first step that migrants take before crossing international borders (Wesselbaum et al., 2021[92]).
Internal migration is a key driver of urbanisation
Internal migration to urban areas is a key element of urbanisation. While rural areas remain relevant as reception hubs in some regions (mostly due to rural-rural migration, and also a sizeable share of urban-rural movements in some cases), migration to urban areas represents an important share of internal migration across the board. Indeed, internal migration is one of the largest contributors to the growth of urban areas, along with natural population growth. For low- and middle-income countries, close to 40% of the urban growth rate stems from migration (Rigaud et al., 2018[86]; Tacoli et al., 2014[8]); see also (Montgomery, 2008[93]). Intermediary cities are projected to receive 400 million new inhabitants from 2014 to 2030, with more than 90% of this migration taking place in Asia and Sub-Saharan Africa – the equivalent of almost 70 000 rural migrants daily (Roberts et al., 2016[24]).
Rapid rural-to-urban migration has occurred particularly in countries that have undergone fast structural transformation, such as China, South Korea, Viet Nam and Thailand. For example, China’s fast structural transformation process led to a decline of the rural population from 80% to 55% between 1992 and 2012, and the number of internal labour migrants reached 260 million, accounting for 19% of the country’s total population (Lucas, 2015[94]). These changes took place in spite of Chinese government efforts to limit rural-to-urban migration through the hukou system. The development of infrastructure and communication services have been key drivers in the growth of rural-to-urban migration. As these services reduce the distances between rural and urban areas, rural households are now increasingly mobile and live along the rural-urban interface (FAO et al., 2018[1]).
Intermediary cities are often the preferred destination of migrants due to the low costs associated with moving to these urban centres. Migrants tend to look for better economic opportunities, and choose their destination based on factors including credit constraints and travel costs (Waldinger and Fankhauser, 2015[95]; IOM, 2008[88]). Indeed, migration costs (economic, social and psychological) are a key factor in rural migrants’ choice of local and short-distance destinations. In Tanzania, for instance, lower migration costs were a key factor leading to migration towards small and medium-sized cities, as noted by Christiaensen et al. (2013[96]). Long-distance migration involves higher travel costs and greater difficulty in accessing information about the chosen destination (FAO et al., 2018[1]). Women can be especially constrained by migration costs, as they may not have access to personal transport (McOmber, 2020[97]).
Accessible information, pre-existing networks and cultural ties make intermediary cities attractive to migrants. Rural migrants may have better access to information regarding possible socio-economic opportunities in closer intermediary cities than in farther destinations. Pre-existing networks (like friends and relatives) may also play a role in incentivising migrants to travel to nearby cities and towns. Indeed, in rural Gujarat, India, the presence of family networks in urban areas was associated with the likelihood of rural out-migration for young men experiencing land scarcity and water depletion (Fishman et al, 2013[98]). Similar cultures, ethnicities, languages and social networks may play a role in keeping migration to shorter distances. The smaller the distance, the higher the likelihood of cultural similarities, making it easier for migrants to integrate, potentially exposing them to less xenophobia and possibly giving them a social safety net to fall back on (IOM, 2008[88]; Fishman et al, 2013[98]; Fafchamps and Shilpi, 2013[99]). Indeed, evidence in Nepal shows that much internal migration tends to be short distance, to nearby higher-density areas, because migrants prefer to go to areas with networks and similar language and culture (Fafchamps and Shilpi, 2013[99]; Burrows and Kinney, 2016[100]).
Rural migrants are more likely to relocate to informal settlements. Because of resource constraints and established networks, rural migrants are overrepresented in such settlements (Forefight, 2011[101]; Walter, 2015[102]; IDMC, 2017[103]; Bolay, 2020[104]; Mohit, 2012[105]). While informal settlements are most often associated with large metropolitan cities, especially in South Asia and Sub-Saharan Africa, the rapid, unplanned urbanisation now occurring in many intermediary cities in the developing world creates a ripe context for the spread of informal settlements (Roberts and Hohmann, 2014[25]; Williams et al., 2019[106]). These settlements often lack access to basic services such as electricity, running water, sanitation or waste management.
Internal migration can be a coping strategy for climate shocks
Internal migration can be considered a type of adaptation strategy that serves to manage risks, diversify income and smooth consumption levels in the face of shocks. Some experts see migration as a last resort coping strategy, one that denotes failure to adapt (IOM, 2020[107]; Banerjee, 2016[108]; Hummel et al., 2012[109]). However, this does not take into account that migration (including internal migration) is also a livelihood strategy, i.e. a way to accumulate assets (Hummel et al., 2012[109]; Tacoli, 2009[110]).
Migration does not necessarily denote the relocation of an entire household. Instead, households, especially rural households, can send one or more family members to migrate (Rigaud et al., 2018[86]; Stark and Bloom, 1985[111]; Rain, 2000[112]). This can be dynamic and does not have to be for an indefinite time period. There is extensive evidence of seasonal, or circular, migration, where an individual migrates away for a period of up to six months, and eventually returns home and re-integrates into the household’s activities, as described in Box 3.9 (Hummel et al., 2012[109]; Findley, 1994[113]). Who migrates, where they migrate and for how long are questions determined by local socio-economic factors, as well as by patterns of vulnerability in places of origin (Tacoli, 2011[114]).
Box 3.9. Circular migration as a livelihood strategy
Circular or seasonal migration is an important coping strategy for many rural households seeking to diversify income and to reduce poverty, food insecurity and the effects of agricultural output fluctuations. Temporary migration is also an inherent part of the livelihoods and lifestyles of pastoralist groups.
Seasonal and circular migration as a coping mechanism is common across many regions and countries. In Lichinga, Mozambique, for example, non-linear migration patterns are frequent. The city of approximately 213 000 hosts more people in times of drought and excess rains than during the harvesting period, when men and women of working age often return to rural areas (UCLG, 2016[115]). A similar phenomenon is seen in Mzuzu and Blantyre in Malawi, which like many intermediary cities have larger daytime than nighttime populations (UCLG, 2016[115]; ARUP and Cities Alliance, 2016[116]). However, unlike in Lichinga, the elderly in Malawi are more likely to stay in rural areas (UCLG, 2016[115]). Findings from Mali, Ethiopia, Senegal, Argentina and India highlight that these migration strategies are seen as coping mechanisms, especially towards the end of the growing season (FAO et al., 2018[1]).
In India, despite seemingly low levels of urbanisation, people living in rural areas rely heavily on urban family members (Dyson, 2004[117]; Rigaud et al., 2018[86]). In Bangladesh, seasonal or circular migration is a common coping strategy when alternative measures are not available (Khandker et al., 2014[118]). In northern regions of Bangladesh, 36% of the extremely poor adopted seasonal migration in 2006 to mitigate or cope with hunger.
There are numerous benefits of circular or seasonal migration, especially for rural households. Having fewer household members can help reduce household expenses (Dillon et al., 2011[119]). Migration also facilitates the sending of remittances back to the household from different locations, particularly urban areas, helping to smooth consumption levels (Dillon et al., 2011[119]; Rosenzweig and Stark, 1989[120]). Migration that diversifies risk is occasionally deemed to be a result of income variability in a given location, as will be discussed in more depth below (Lilleør and Van den Broeck, 2011[121]).
Other coping strategies can complement or even substitute for migration from rural areas. In some cases, on-farm and off-farm strategies can serve as alternative adaptations to migration. Off-farm adaptation includes strategies such as starting small businesses or engaging in wage labour. This can diversify income streams and, depending on the business or employment, can diversify the sectors in which families or individuals are engaged, which is important as it can reduce the climate vulnerability associated with agriculture (FAO, 2016[122]). On-farm adaptation can include strategies such as climate-smart agriculture, agricultural intensification, crop adaptation, irrigation or changing species in livestock rearing (FAO, 2016[122]). Studies in Ghana show that on-farm adaptation such as irrigation and crop rotation may substitute for migration, as households that successfully implement these adaptations send fewer migrants (Antwi-Agyei, Stringer and Dougill, 2014[123]; Laube, Schraven and Awo, 2012[124]). As such, it is important understand that migration is not always the first or the preferred choice.
Climate change is expected to influence internal population movements
There is increasing evidence that climate change is a driver of internal migration in the developing world, and will continue to be in future (Rigaud et al., 2018[86]; Cattaneo et al., 2019[125]; IPCC, 2014[126]). Recent estimates suggest that, without action to reduce greenhouse gas (GHG) emissions, internal climate migrants could number 216 million by 2050 across regions including Sub-Saharan Africa, North Africa, South Asia, East Asia and the Pacific, Eastern Europe and Central Asia, and Latin America. This represents 3% of the projected total population of the six regions (Clement et al., 2021[127]). Migration trends may intensify after 2050 as climate impacts worsen in tandem with a rise in population across some regions (Rigaud et al., 2018[86]). Another study, drawing from case studies in Tanzania, Senegal and Bolivia, also finds that “climatic and environmental pressures” may cause hundreds of millions to seek shelter or leave their homes by 2050 (Tacoli, 2011[114]). However, accurately measuring the effects of climate change on internal migration is challenging and complex, due to the complex and interlinked factors that can prompt migration (Box 3.10). Estimations from recent studies on climate-induced migration, such as Clement et al. (2021[127]), are likely to be rather conservative because they may only consider slow-onset events driven by changes in water availability, crop production and sea-level rise caused by storm surges, and because they do not account for some regions, such as North America, Europe, the Middle East and small island states (Clement et al., 2021[127]).
Box 3.10. Measuring the impact of climate change on internal migration
Analysing the effects of climate change on internal migration for policy purposes is challenging. This is due to the following factors:
Identifying who is a climate migrant is complex, as separating the effects of climate change from other migration drivers is challenging. This is because the decision to migrate is itself highly complex and is influenced by other interrelated factors, including economic, political, demographic, social and environmental factors (Rigaud et al., 2018[86]; Walter Kalin, 2010[128]; Brzoska and Fröhlich, 2016[129]). Making matters more complicated is the finding that people seldom move strictly due to climate conditions or even “altered ecosystem services” (IPCC, 2014[126]), with the exception of fast-onset events and displacement (Brzoska and Fröhlich, 2016[129]; Castles, 2002[130]).
It is also difficult to predict what climate change-induced migration may look like. First, limitations in data and difficulties in methodology make it challenging to deduce the scale of existing internal migration ( (Rigaud et al., 2018[86]; Tacoli, 2011[114]). It is also difficult to predict the locally specific effects of climate change, which may be dynamic and change from period to period depending on the region (Tacoli, 2011[114]).
Despite the uncertainties, current research on the links between climate change and migration can play an important role in national, regional and municipal planning. As indicators of future migration, these links are best regarded as short-term estimates (Cattaneo et al., 2019[125]). Given the current unprecedented urbanisation in the developing world, anticipating trends in climate-influenced migration is important to help prepare areas for coming inflows of migrants.
In assessing the impact of climate change on migration decisions, it is important to understand the ways in which climate affects the household’s environment. Climate change will cause rising temperatures, rainfall variability (too much or too little rainfall), degradation of soil quality and ecosystems and prolonged drought, and will result in extreme climate events such as storms and flooding (FAO, 2016[122]). As such, there is an important distinction to be made between fast-onset and slow-onset climate events (Rigaud et al., 2018[86]; IOM, 2008[88]; Cattaneo et al., 2019[125]). Fast-onset or extreme weather events include floods, storms, and cyclones, whereas slow-onset events include rainfall variability, seasonal variations and rising temperatures, among others (Table 3.1).
Table 3.1. Fast-onset and slow-onset climate events and migration
Fast onset |
Slow onset |
|
---|---|---|
Type of climate events |
Extreme sudden climate events: floods, storms, cyclones, etc. |
Long-term changes/shifts in climate: rainfall variability, seasonal variations, temperature rise, droughts, etc. |
Direct vs. indirect effect on migration |
Direct effect |
Indirect through effects on existing social and economic drivers of climate change |
Common type of migration |
Involuntary internal displacement/fast migration trends |
Slower pace migration trends, with permanent changes in livelihoods or locations |
Migration distance |
Short distance |
Short distance, or longer distance based on migrant economic conditions |
Migration period |
Temporary |
Permanent |
Source: Author’s elaboration.
Climate change is expected to impact rural livelihoods in many ways, and the speed with which households react to climate events depends on the nature of the event. Climate disasters increase the risk of fast displacement as they can cause the destruction of crops and livelihoods (IPCC, 2014[126]). On the other hand, slower events such as increasing temperatures can decrease crop productivity, reduce water availability and cause land degradation, among other impacts. This in turn can lead to resource constraints, create tension in communities and make it more difficult to engage in traditional livelihood activities (Selby et al., 2017[131]), which could delay the migration decision.
Changes in precipitation are expected to have a large impact on migration decisions. Recent evidence suggests that a decline or negative shock in rainfall is associated with an increase in internal and international migration (Zaveri et al., 2021[132]).3 Estimates suggest that, between 1970 and 2000, declines in rainfall have accounted for 10% of the increase in migration globally. However, there are important nuances in the rainfall-migration nexus. The effects of changes in rainfall on migration patterns are highly dependent on a country’s income level. Out-migration tends to take place primarily in rural areas in low-income countries, which tend to be highly reliant on agriculture as a source of livelihood and sustenance.
Climate change can also cause immobility. Slow-onset events harm agricultural productivity and curtail the available resources of households (Selby et al., 2017[131]), reducing their financial capacity to migrate (Cattaneo et al., 2019[125]; Kleemans et al., 2014[133]; IPCC, 2014[126]; Robalino et al., 2013[134]). This is particularly relevant in low-income countries, where high temperatures have been found to have a negative effect on rural-to-urban migration, whereas in middle-income countries climate change has spurred migration. One possible mechanism behind this effect is that low rural income acts as an incentive to migrate, but that when it is too low it can act as a constraint (Peri and Sasahara, 2019[27]). Estimates suggest that the poorest households are less likely to migrate in circumstances of rainfall scarcity. As such, in circumstances of extreme poverty, declines in rainfall are more likely to trap populations than to induce migration (Zaveri et al., 2021[132]).
Migration trends and directions can also be disrupted by non-climate-related shocks. COVID-19 and the restriction measures implemented by governments caused large disruptions in internal migration trends, especially in developing countries. This process did not come without challenges since it especially impacted vulnerable internal migrants. Some of the main pandemic-related challenges faced by internal migrants are presented in Box 3.11.
Box 3.11. Effects of the COVID-19 pandemic on internal migrants in developing countries
COVID-19 and the restrictions imposed to contain the spread of the virus have posed and continue to impose enormous challenges on (vulnerable) internal migrants, especially in developing countries. Overall, far less research has been conducted on the effects of COVID-19 on internal migration than on food systems. Indeed, the shortage of adequate data and evidence on the numbers of internal migrants makes it challenging to gain accurate understanding of the effects on COVID-19 on their mobility and well-being. Yet studies of countries like India have demonstrated the deep effects on internal migrants of the sanitary and economic crisis caused by the pandemic (Gupta, 2020[135]).
COVID-19 disproportionality affected internal migrants due to their socio-economic vulnerabilities. Internal migrants tend to be overrepresented in informal sectors such as domestic work, seasonal work and street vending. As such, a large share of internal migrants lost their livelihoods and sources of income in the pandemic, and restrictions on mobility prevented them from returning to their places of origin, leaving them stranded in poor-quality and overcrowded housing or slums (Rajan et al., 2020[136]). COVID-19 and restrictive measures also led to an exodus of migrants from urban to rural areas.
India faced some of the largest challenges in protecting its internal migrants. India had an estimated 600 million internal migrants in 2020 (Rajan et al., 2020[136]). Out of this total, 200 million were interstate and inter-district migrants, with internal migrant workers accounting for two-thirds (or 140 million) of the interstate migrants (Gupta, 2020[135]; Rajan et al., 2020[136]). Indian internal migrants, who are often from lower-income quintile groups and disadvantaged caste groups, are often excluded from social safety nets, and thus face significant challenges from short-notice lockdowns and restrictions on mobility (Rajan et al., 2020[136]). During the pandemic, India’s internal migrants found themselves stranded in urban areas with no source of income and no transportation for their journeys back to their families. Women internal migrants faced disproportionate challenges, as four in ten women lost their livelihoods within two months of the COVID-19 outbreak (Rukimini, 2020[137]). Some 30% of the migrants who left Indian cities are not expected to return post-pandemic, and their absence is expected to cause significant economic losses (Gupta, 2020[135]). In African countries, such as Uganda, Madagascar and Kenya, meanwhile, migrants undertook long journeys back to rural areas, in some cases by foot, to avoid even more economic strains and, in many cases, hunger (The Citizen, 2020[138]).
COVID-19 brought to the surface the structural challenges that internal migrants have long faced and their disproportionate vulnerability to these challenges. Moreover, the loss of livelihoods among internal migrants will have rebound effects on larger population groups. Internal migrants are critical in the flow of remittances, especially to rural households, which rely heavily on remittances for their daily consumption (World Bank, 2021[139]). Going forward, establishing an adequate evidence base (i.e. data) on the numbers and conditions of internal migrants is the first step to improving their well-being. In addition, local and national governments should establish measures to protect vulnerable internal migrants and to integrate them into local and national safety-net programmes.
Intermediary cities are often the main destination of climate-induced migration
Urban areas will increasingly be the main destination of internal migration in the face of climate shocks. As climate negatively affects agricultural productivity, and earnings, rural migrants will be encouraged to look for opportunities elsewhere (Cattaneo et al., 2019[125]). Studies have highlighted the links between declining agricultural productivity and migration due to variations in temperature in both Sub-Saharan Africa (Marchiori et al., 2012[140]) and India (Viswanathan and Kumar, 2015[141]).
People who resort to migration in search of opportunities and better livelihoods usually move to urban areas. As the primary destination for population movements resulting from changes in rainfall patterns, cities face the double burden of providing water services to a growing population while coping with a decline in water supply. By 2050, water demand in cities is expected to rise by 80% from current levels (Flörke et al., 2018[142]). Cities with a large metropolitan area, such as Cape Town (South Africa), Chennai (India) and São Paulo Brazil, have attracted international attention for approaching a “day zero” of water availability: the moment when water taps have to be shut down for lack of water (Zaveri et al., 2021[132]). Water decline slows economic activity and development processes; in the most extreme cases, cities can lose up 12 percentage points in GDP (Zaveri et al., 2021[132]). However, the effects of a decrease in rainfall have nuances depending on geography and city size. For instance, cities in arid areas tend to be better equipped to handle abnormally lower rainfall than cities in humid areas. Similarly, large cities tend to be more resilient against lower rainfall than small cities (Zaveri et al., 2021[132]).
The effects of climate change on the dynamics of internal migration will have indirect effects on intermediary cities. Climate change will escalate pre-existing migration flows to close and familiar urban centres, exacerbating pressures on intermediary cities and their urbanisation process – first and foremost by increasing the demand for infrastructure, but also by expanding social and economic inequalities; creating health concerns (IOM, 2008[88]); and increasing the pressures facing institutions that are already under strain. Nevertheless, the inflow of migrants could also become an opportunity for development.
Understanding the different ways in which climate change will impact patterns of migration and the needs of migrants is essential for intermediary cities to prepare for new challenges. The next sections will consider the different types of migration caused by climate events and the vulnerabilities migrants face upon arrival. There is scope for intermediary cities to act on these dynamics in advance and improve the well-being of both urban dwellers and newcomers.
Fast-onset events in particular can spur migration towards intermediary cities
Fast-onset events affect developing economies disproportionately and are a major reason for internal displacements. According to the Internal Displacement Monitoring Centre (IDMC), the countries with the greatest number of new internal displacements in 2019 were India, Philippines, Bangladesh and China. Natural disasters triggered the displacement of the more than 17 million people who migrated in these countries in 2019. Of the total number of natural disasters that impacted the world in 2019, 96% were weather related. Figure 3.8 shows the number of new displacements due to weather-related events in developing countries. Storms were the major cause of these movements, followed closely by floods.
Fast-onset events are linked to short-distance migration, with intermediary cities likely to be the destination of displaced people, particularly from nearby rural areas. In the face of extreme weather events, migrants are forced to make decisions very quickly and are likely to choose familiar places where they may have networks (Waldinger, 2015[143]; Brzoska and Fröhlich, 2016[129]). They may stay only temporarily at these destinations: there is strong evidence that many displaced migrants have an intent to return (Rigaud et al., 2018[86]; Burrows and Kinney, 2016[100]; Brzoska and Fröhlich, 2016[129]; Walter Kalin, 2010[128]; Black et al., 2011[144]; Castells-Quintana, Del Pilar Lopez-Uribe and Mcdermott, 2015[145]).
Depending on the extent of the damage caused by an extreme weather event, displaced migrants may slowly repopulate their original towns or villages after first living in a nearby city or town as an intermediary destination. This was observed in the United States following Hurricane Katrina, which struck New Orleans and its surrounding area in 2005 and forced many people to evacuate. Over a five-year period, New Orleans recovered its population as the displaced returned indirectly by migrating across one or more intermediary destinations (DeWaard et al, 2016[146]).
In some cases, intermediary cities receive migrants affected by fast-onset events in a delayed fashion. People may arrive in nearby cities and towns looking for work in the aftermath of the disaster, possibly because of loss of livelihood or debts from loans for damage repair. After severe rural flooding in 2014, Bangladesh saw an increase in migration, especially of males, for these reasons (Walter, 2015[102]).
Intermediary cities may also receive migrants from other urban areas in response to fast-onset events. For example, flooding due to rising seas and water scarcity affecting large coastal cities in South America may lead migrants to relocate to small and medium-sized cities (Warn and Adamo, 2014[147]). There is evidence in Costa Rica that less severe emergencies in metropolitan areas (those that do not result in death) can also increase migration to other metropolitan areas (Robalino et al., 2013[134]). Migration from large urban areas towards intermediary cities could be driven by the fact that many very large cities are severely stretched in terms of physical infrastructure, services, employment and housing.
Socio-economic factors play a role when migrants take refuge in intermediary cities during fast-onset climate events. Rural migrants may initially take up residence on the outskirts of urban centres due to the lower up-front costs. Since such settlements are often informal, this increases their vulnerability (IDMC, 2017[103]; Bolay, 2020[104]). Informal settlements are often poorly serviced, with no drainage systems or sewerage and little access to public services. Migrants who settle there, become vulnerable to events like floods and storms, making their displacement maladaptive (IOM, 2020[107]; McAdam and Ferris, 2015[148]).
Slow-onset events can spur migration towards intermediary cities for longer periods
Slow-onset climate events tend to influence internal migration indirectly via channels categorised as “economic” and “social”, with migration taking place more slowly than with fast-onset events (Rigaud et al., 2018[86]; Lilleør and Van den Broeck, 2011[121]; Cattaneo et al., 2019[125]) (FAO et al., 2018[1]).
Economic channels” concern migration resulting from economic downturn. For instance, climate variability can reduce agricultural productivity and yields, which in turn decreases rural incomes. This is the case across rural areas of Sub-Saharan Africa, South Asia and Latin America, where agriculture – the basis of a large share of rural livelihoods – is highly vulnerable to climatic changes such as variability in rainfall or extreme wet or dry conditions, including droughts (Cattaneo et al., 2019[125]; Burke et al., 2015[149]; Dallmann and Millock, 2017[150]). These risks have the potential to decrease income in rural areas and eventually drive migration. Indeed, the people most vulnerable to climate change are those with income directly linked to agriculture (Cattaneo et al., 2019[125]). Box 3.12 highlights the subcategories in which migration can be triggered through economic channels.
Box 3.12. Climate induced-migration via economic channels
Income differentials between rural and urban areas is one of the main push factor for internal migration. Income differentials refer to wage differences between origin and destination, such as rural and urban areas (Cattaneo et al., 2019[125]). Research that builds on classic models of rural-to-urban migration found that expected increases in income associated with moving from rural to urban areas facilitate migration, even when considering the reality of widespread high unemployment in a destination city (Harris and Todaro, 1970[151]).
Climate change is affecting income differentials, which has contributed to spur urbanisation in the developing world. In Sub-Saharan Africa, evidence has shown that weather anomalies causing low agricultural productivity and rural earnings resulted in rural-to-urban migration, contributing to urbanisation (Marchiori et al., 2012[140]). A study in India showed that climate-induced losses in agricultural output led to interstate migration, working as a push factor for urbanisation (Viswanathan and Kumar, 2015[141]). The study indicated that migration patterns may vary depending on the main crops grown in the region, with the effect of out-migration for ubiquitous labour-intensive crops such as rice found to be higher than for wheat (Viswanathan and Kumar, 2015[141]). However, development and increased income can also facilitate mobility and migration. For example, development may be a factor in increased rural-to-urban migration in India from 1991-2001 compared to previous decades (Viswanathan and Kumar, 2015[141]).
Climate change could also spur migration through income variability. In this case, migration can occur in anticipation of or in response to changes or fluctuations in income over time (Cattaneo et al., 2019[125]). However, there is little evidence for climate-related income variability in rural areas contributing to migration. For example, a macroeconomic study of 39 Sub-Saharan African countries showed “negligible” impact of income variability on migration (Marchiori et al, 2017[152]); (Lilleør and Van den Broeck, 2011[121]; Cattaneo et al., 2019[125]). In contrast, a study on Nigeria found some evidence for income variability leading to migration. The study used household-level surveys in northern Nigeria to measure income variability through temperature and found that households had sought to smooth their consumption patterns both in response to and in anticipation of income variability caused by an idiosyncratic climate shock (Dillon et al., 2011[119]). Nigerian households were likely to send a male household member to migrate as a risk-management strategy to diversify income, with suggestive evidence for migration decisions ex-ante, in anticipation of a decrease in wages, and robust findings for migration decisions in response to risk and variation in wages (Dillon et al., 2011[119]).
“Social channels” concern conflicts that act as drivers of migration or result from migration. Conflict and climate change can interact with migration in various ways. Climate-induced migration can lead to resource scarcity in migrant destinations. This can spur tensions and competition for livelihoods, ultimately causing conflicts. Eruption of conflicts in migrant destinations can potentially trigger further migration through forced displacement. However, there is little agreement on the idea that climate change has a causal effect on conflict, and more specifically that climate-change-induced migration causes conflict (Burrows and Kinney, 2016[100]; Hsiang, Burke and Miguel, 2013[153]; Reuveny, 2008[154]). Box 3.13 highlights the ways in which climate change, migration and conflict can interact, especially in Sub-Saharan Africa.
Pre-existing networks and resource constraints are likely to influence the decision to migrate to urban areas in the case of slow-onset events. Unlike migration due to fast-onset events, when decisions must be made quickly, people migrating due to slow-onset events are likely to take account of travel costs, family ties and social networks. Moreover, while there is evidence, in India for example, of high rural-to-rural migration in times of drought, it may be that declining wages in rural areas mean that they offer fewer options than urban areas (Dallmann and Millock, 2017[150]).
Intermediary cities and towns near rural areas are likely to host rural migrants over a long period of time. Migrants whose livelihoods have been negatively affected will be motivated to leave rural areas (sometimes permanently) in search of new livelihoods. This is a continuation of a pre-existing practice of flocking to nearby cities and towns looking for ways to supplement income, diversify risk and secure nonfarm labour (Roberts and Hohmann, 2014[25]). If there are opportunities, then the migrants are likely to stay, especially if climate factors continue to constrain natural resources and diminish agricultural productivity (IOM, 2020[107]; UNEP, 2011[155]; Bolay, 2020[104]).
As a result of these interrelated factors, intermediary cities should expect an increase in the arrival of rural migrants looking for work. Upon reaching urban areas, rural migrants may look for work opportunities in construction and transport, including informal work; in work associated with natural resources, such as mining; or in farm-related employment such as agricultural processing and value-add services (McOmber, 2020[97]; Agergaard et al., 2019[37]; Christiaensen and Todo, 2013[96]). With greater reliance on self-employment in the midstream segments of food systems (Dolislager et al., 2020[156]), women who migrate to urban areas can be overrepresented in informal food markets in intermediary cities.
Due to resource constraints, migrants are likely to settle in the outskirts of town or informal settlements, where they face health risks including increased exposure to vector-borne diseases, such as malaria and dengue fever, and the risk of bodily injury. They may also lack access to transportation. Although migrants often choose to move to intermediary cities rather than large urban areas, mobility may be an issue while living on the edges of a city (Waldinger, 2015[143]; Bolay, 2020[104]). There is evidence that migrants in cities with less access to transportation services face higher costs and greater challenges (Waldinger, 2015[143]). They may find the city hard to navigate and have less access to nearby services.
Box 3.13. Climate change, internal migration, and conflicts
The links between climate change, internal migration and conflicts are complicated and vary significantly by local history and socio-economic circumstances. However, there is evidence that increasing pressure on environmental resources could result in mounting tension and migration. These findings are in line with neo-Malthusian theories that environmental and economic scarcities increase tensions and result in pressure or competition (McOmber, 2020[97]; Burrows and Kinney, 2016[100]). Extensive evidence in West Africa confirms the contribution of climate change to tensions due to pressure on natural resources. In some of these instances, the resource constraints themselves have been a result of climate change (Burrows and Kinney, 2016[100]). Dry conditions in Mali, Niger, and Nigeria resulted in shrinking land available to pastoralists, pushing them to search for land. This resulted in increased competition and tensions with agriculturalists, who were already increasing their land use to meet the growing food demand in the region (UNEP, 2011[155]; Dominic Azuwike, 2010[157]; Keith Moore, 2005[158]). In Mali and Nigeria, these tensions resulted, respectively, in low-level conflict and violence (UNEP, 2011[155]; Warner et al., 2009[159]; Mwiturubani and Van Wyk, 2010[160]).
Climate-induced migration driven by conflict is context specific. For instance, in the Lake Chad region, drought conditions pushed some agriculturalists to conduct fishing activities. This compounded the scarcity faced by existing fishermen as a result of the shrinking size of the lake, as well as overuse and population growth (UNEP, 2011[155]). It may have been exacerbated by new migrants to the area who arrived looking for work (UNEP, 2011[155]). A result was migration by those whose livelihood depended on the lake and those whose livelihoods were connected to business with fishermen (UNEP, 2011[155]).
In some instances, conflict can also limit the scope for migration responses to climate change. For instance, pastoralists in the Horn of Africa tried to respond to drought by migrating in search of better water resources but faced restrictions of movement due to conflict (ICRC, 2004[161]). McGuirk and Nunn (2020[162]) find that climate changes in Africa can lead to conflict between pastoralists and sedentary agricultural groups, particularly during rainy season. This is because changing rain patterns and rainfall shocks can push pastoralist groups to migrate to agricultural land before harvest time, which leads to crop damage due to premature grazing.
Overall, the climate change-conflict-migration pathway is more often than not associated with economic factors or resource scarcity. In this regard, climate change does not directly cause conflict but affects other variables that trigger conflicts. A leading counterargument against climate change-induced migration causing conflict is that there are a great many examples of migration without conflict, and that associating a security risk with migration may be political in nature and therefore requires caution (Burrows and Kinney, 2016[100]).
Climate-induced migration affects the urbanisation process of intermediary cities
The linkages between climate patterns and migration are manifold and complex. As discussed above, climate change can push people to relocate across the rural-urban continuum in search of employment, particularly from rural areas to cities, yet with slow-onset climate events people who would have otherwise migrated might postpone their decision or not migrate at all (Selby et al., 2017[131]). The effects of climate on migration also depend on the ability of people to migrate and the ability of a city to absorb migrants.
Climate-induced migration is expected to contribute to urban population growth in developing economies. Barrios et al. (2006[163]) find that shortages in rainfall are associated with an increase in urbanisation rates in Sub-Saharan Africa. However, there are nuances. Henderson et al. (2017[164]) find that drier conditions influence migration to urban areas only if the city can absorb the excess workers from agriculture into its manufacturing industry. If the city is not a manufacturing centre (less industrialised), drier conditions tend not to affect urbanisation levels.
The effect of changing climatic patterns on urban population will depend, among other things, on baseline climatic conditions. Recent evidence on capital cities suggests that the relationship between climate change and urban population depends on whether a city already experiences high temperatures and dry conditions: the effect of rising temperatures on urban population growth is higher in places that are already hot, and the effect of lower precipitation on population growth is higher in places that are already dry (Castells-Quintana, Krause and McDermott, 2021[165]).
Similar patterns are also observed in the case of intermediary cities. Figure 3.9 (left side) presents the results of an econometric analysis looking at the relationship between temperature and urban population among cities of different sizes in developing countries (detailed results in Tables 3.A.1 and 3.A.2 in Annex 3.A1). The figure shows that the relationship between temperatures and population growth follows a U‑shape, suggesting that higher temperatures tend to have a stronger effect on population in places that are already warm, and that this relationship is more acute for intermediary cities.
Changing rain patterns also have an impact on the population of small and medium-sized cities. Figure 3.9 (right side) shows the relationship between precipitation and population. As in the case of temperature, this relationship is not linear. However, this relationship is only statistically significant for cities of fewer than 500 000 inhabitants and – in contrast to temperature – follows an inverted U-shape (detailed results in Tables 3.A.1 and 3.A.2 in Annex 3.A1). It suggests that in small and medium cities, higher levels of precipitation are associated with an increase in population.4 The shape of the curve also indicates that dry places are more sensitive to changes in rain patterns, i.e. that more precipitation tends to have a positive and larger effect on population in dryer places.
Climate change can also influence the distribution of the population across a city’s functional urban area (FUA). Functional Urban Areas refer to functional economic spaces that reflect population density and commuting flows, and that interconnect urban centres that are part of the same functional areas (OECD, 2018[167]). As argued throughout the chapter, cities need to be understood in the context of rural-urban linkages and interactions, as they are not individual entities but are deeply connected to their surroundings. In the context of this analysis, urban share is considered as the ratio between a city’s population in its high-density core and that of its total functional area. The FUA provides relevant information on the spatial structure of cities. A high value of the ratio indicates that more people are concentrating in high-density areas, while a low ratio means that the population of the FUA is more evenly distributed across high-density and low-density areas. The results in Tables 3.A.3 and 3.A.4 in Annex 3.A1 show that, in the case of cities of 50 000 to 100 000 inhabitants, rising temperatures have a negative relationship to urban share (albeit with decreasing returns). This suggests that, as temperatures increase, fewer people concentrate in high-density areas. This relationship is not statistically significant for bigger cities. Moreover, changes in precipitation do not seem to have a significant effect on urban share.
The effects of changing climate patterns on urban population are far from simple. The results above suggesting that an increase in temperatures will drive up population in warm cities is in line with the literature that relates higher temperatures to rural out-migration into urban areas (Arslan, Egger and Winters, 2018[14]). Moreover, these results reinforce previous findings about the complex effect of climate change on population, which can depend on baseline income levels (Zaveri et al., 2021[132]) as well as on baseline climatic conditions (Castells-Quintana, Krause and McDermott, 2021[165]). The analysis shows that the positive effect of higher temperature on urban population will only take place in those cities where average temperatures are already high. The effect of changing rain patterns on population is even more nuanced and the results suggest that this also depends on city size. Castells-Quintana, Krause and McDermott (2021[165]) find that drier conditions in already arid places drive up population in large cities. Focusing on smaller urban centres, as in this analysis, shows that an increase in precipitation levels has a positive effect on urban population only among cities with fewer than 500 000 inhabitants. This effect is more important for cities located in dryer places.
Strengthening the climate-change resilience of the rural-urban interface
Adopting territorial policies can be a critical step for building effective climate policies that account for rural-urban linkages. Building long-term resilience in the rural-urban interface, while tapping into the potential of cities for climate mitigation, calls for a shift in policy perspectives in both urban and rural areas. As global economies recover from the economic and sanitary crises created by COVID-19, and climate change becomes a pressing global concern, there is a need to adopt policy approaches that are people centred and place based, and to redefine policy goals to obtain wider well-being outcomes, such as improved health. Adopting territorial approaches for intermediary cities can help to achieve these goals, while strengthening rural-urban linkages and reducing regional and spatial inequalities (UN-Habitat, 2019[168]), as well as promoting policy coherence across sectors and territories (Hussein and Suttie, 2016[36]).
Adopting territorial approaches and building a resilient rural-urban interface cannot be successful without the full engagement of the national government and effective co‑operation across all levels of government. Multi-level dialogue across all levels of governments (central, district or regional, and city or local) is key for designing policies that address the direct and indirect effects of climate change. Local governments play a critical role in implementing climate policies, as they have better awareness of the specific challenges faced. However, by unlocking resources and co‑ordinating efforts, national governments play a fundamental role in supporting and scaling up local governments’ climate actions. Many local governments that manage intermediary cities lack the technical and financial capacity, as well as the political autonomy, to maximise opportunities for adaptation and to implement actions to mitigate climate threats (Satterthwaite, 2016[26]). National governments have larger scope to support intermediary cities in establishing climate actions and unleashing the socio-economic gains that green growth strategies may provide (Coalition for Urban Transitions, 2021[169]). However, this will require closing the gap between local ambitions and national visions, while providing tailored support to implement strategies for disaster risk reduction. Box 3.14 highlights the challenges faced by local authorities in Quelimane, Mozambique, in their efforts to implement climate actions without support from the national authorities.
Box 3.14. Implementing climate actions in Quelimane, Mozambique
Quelimane, with approximately 400 000 inhabitants, is the fourth largest city in Mozambique. Its high vulnerability to increasingly frequent climate-change threats, especially floods, is causing large socio-economic challenges, including losses in agricultural production, land and water scarcity, infrastructure damage, etc. (Plataforma, 2020[170]). The local government has implemented climate actions to reduce the effects of extreme events. However, Quelimane’s government faces challenges that diminish its capacity to intervene effectively, especially constraints in institutional capacity. These include:
inadequate legislative framework. Local authorities do not have sufficient powers to deal with extreme climate-related events. This shows the need to strengthen legislative frameworks to enhance cities’ capacity to respond to extreme events.
low human resources. Local governments do not have the human resources needed to understand and create a policy framework for implementing actions to address climate threats.
lack of adequate dialogue and co-operation. In most cases, local governments have no capacity to influence national goals and strategies and lack transparent dialogue with regional and national governments and international partners. National governments do not establish adequate spaces to enable local governments to access information or contribute to discussions at national level.
lack of access to information. Despite being at the forefront of climate actions, local governments tend to have inadequate understanding of the relationship and dynamics between rural and urban areas, and are unprepared to manage pressing issues such as the increase in rural-urban migration, which contributes to the expansion of informal settlements and unemployment.
Source: Intervention by Mayor Manuel de Araujo of Quelimane, Cities Connect Expert Meeting, 2020.
As the Quelimane example makes clear, it is key that national governments provide the right incentives to city authorities to work collaboratively on a regional scale. Such collaboration allows leaders to tap into their own city’s assets while also drawing upon the wider region’s assets, scale and expertise (Jeffrey, 2017[171]). In order to build resilient urban systems, national governments should shift their approach from urban development to systems-based thinking. They ought to envision intermediary cities as part of the urban system and foster stronger partnerships, connectivity and collaboration across cities. This will enable local and national governments to better grasp the socio-economic potential of intermediary cities, as well as facilitating inclusive growth within urban systems. Collaborating with partners such as universities, the private sector, international organisations or other intermediary cities can bring valuable knowledge and experience.
The sections below build on the analysis and findings of Chapters 2 and 3 and propose a series of policy actions that can help to strengthen the resilience of the rural-urban interface while also tapping into the potential of intermediary cities for effective climate-mitigation actions.
Spatial planning is key for climate mitigation and adaption in intermediary cities
Effective spatial planning is one of the most important aspects of climate adaptation and mitigation in urban areas. According to the IPCC (2018[172]), a reduction of greenhouse gases requires transitions in four types of system: land and ecosystem; energy; urban and infrastructure; and industrial systems. Intermediary cities can play a transformative role across all four systems. Early and co‑ordinated spatial planning, which links urban form and infrastructure, can shape land-use management and commuting and transportation patterns, which in turn can have a major effect on GHG emissions at city level. Early planning is key, as it avoids the high cost of changing already built urban infrastructure and established urban form (Satterthwaite et al., 2007[173]).
Integrated spatial planning – which incorporates efficiency in energy use, transportation, mixed density, urban sewerage and waste management infrastructure – has been used in policy strategies in some cities and can provide important lessons for other intermediary cities (UN-HABITAT, 2011[174]). Climate mitigation opportunities in low- and middle-income countries will depend significantly on the type of urbanisation process and energy mix in intermediary cities. The rapid built-up expansion of some intermediary cities, in addition to inadequate infrastructure and use of inefficient energy sources (such as wood fuels and other fossil fuel), may limit the mitigation potential of these cities. Yet spatial planning that accounts for integrated infrastructure and land-use planning can help to reduce urban sprawl and commuting distances, as well as discouraging reliance on private vehicles (UN-HABITAT, 2011[174]). Additionally, incorporating Functional Urban Areas (FUA) or a metropolitan approach to spatial planning can aid local and national governments to better co‑ordinate and enable them to pool resources for joint investment. In turn, national governments can facilitate a metropolitan approach to spatial planning by facilitating and supporting horizontal co‑ordination across lower-level governments (OECD, 2018[167]). Planning compact and high-density urban centres with better access to modern technology and sustainable energy sources can help to reduce GHG emissions. For example, European cities, which tend to be denser than North American cities, produce 50% less CO2. Yet European cities produce double the emissions of Asian cities (Kamal-Chaoui and Robert, 2009[175]) despite being less dense (Gregor et al., 2018[176]). Higher density tends to lead to lower per-capita energy demand, lower CO2 emissions from private transportation and higher demand for public transportation services (IEA, 2016[177]; IEA, 2018[178]).
Integrating infrastructure, housing and mobility policies into spatial planning
As intermediary cities increasingly become migration destinations, spatial planning should consider the implications of a growing local population. Beyond the usual patterns of rural-to-urban migration (including seasonal and circular migration), intermediary cities are also an important destination for displaced populations. Such displacements can result from climate change or conflicts. In Niger, for instance, small cities of 50 000 inhabitants such as Diffa, N’Guigmi, Chétimari and Mainé Soroa received approximately 250 000 displaced migrants due to conflicts in surrounding villages, by 2020 (Wetterwald and Thaller, 2020[179]). The fact that these cities had to host an unprecedented flow of people over a very short period of time put large strains on local basic services, such as housing, sanitation and water, that were already very limited (Wetterwald and Thaller, 2020[179]). With climate-induced displacement expected to continue in the next decades, as global temperatures continue to rise and precipitation patterns change, intermediary cities should prepare for increasing demand for infrastructure, housing and other public services. Overlooking urban population growth in city planning can lead to the expansion of informal settlements, as has occurred in a large share of African cities (Cities Alliance, 2019[180]), and thus increase the climate vulnerability of urban dwellers.
Intermediary cities in developing countries can use the early stages of infrastructure investment to implement instruments that reduce GHG emissions and that can accommodate their growing population. In intermediary cities in low- and middle-income countries, a large share of infrastructure has yet to be built. In Asia, for example, two-thirds of the infrastructure needed by 2050 is still pending (OECD, 2018[181]); this is also the case in Africa (AfDB/OECD/UNDP, 2016[182]). With proper planning, intermediary cities can invest in low-carbon infrastructure in order to reduce the risk of carbon lock-in and avoid a “build now and clean later” approach. Low-carbon infrastructure can contribute to local economic development while also improving the well-being of urban dwellers. It can adapt to and mitigate climate shocks in local food systems by reducing loss and waste in production, thus improving food security. For creating resilient connective infrastructure that is low carbon (such as public transportation services), this would involve building robust storage facilities and improving market stalls. Establishing spatial planning frameworks that aim to reduce urban sprawl and promote compact and accessible urban form can be key to reducing future GHG emissions, while also enhancing the well-being of urban dwellers. Other key elements of long-term urban climate resilience include building effective waste management systems and investment in clean energy supply (Broekhoff et al., 2018[183]).
Intermediary cities are also identified as high-potential areas for improved access to sustainable mobility. Findings from SEforAll (2020[184]) indicate that these urban areas present large opportunities for implementing sustainable mobility solutions and enhancing access to energy in parallel with population growth. However, this requires an integrated planning approach that links land use and transport planning, and that facilitates better mobility and energy use (SEforAll, 2020[184]). Integrating land use, transportation and infrastructure development can help shape travel demand towards lower-emission modes of transportation (Satterthwaite et al., 2007[173]). Investing in low-carbon transportation systems, low-carbon energy supply for domestic use and buildings, and waste management systems can enable local authorities to avoid the higher costs associated with polluting infrastructure (Satterthwaite et al., 2018[185]).
The building sector also provides opportunities for climate mitigation in intermediary cities. Buildings can have energy-saving potential through energy-efficient infrastructure, thermal insulation and recycling and reuse of materials. Adopting nature-based solutions can not only reduce the effects of urban heat islands (UHI), but can also help to reduce the effects of flooding and droughts and enhance water conservation (Satterthwaite et al., 2007[173]).This is particularly pertinent for intermediary cities as they prepare to host increasing numbers of urban dwellers, which will translate into higher demand for housing. Intermediary cities have a window of opportunity now to implement nature-based solutions to reduce the effects of UHI in the future.
Integrating food systems and migration into spatial planning
Integrating food systems into territorial planning is now more important than ever. Food systems are a critical aspect of urban and rural resilience in developing countries, as they are key sources of livelihoods and incomes. For this reason, accounting for food systems, their supply chains and their climate vulnerabilities is critical for building resilience in intermediary cities. This implies implementing strategies that go beyond rural and urban boundaries, and building coherent and sustainable frameworks that stretch across sectors (nutrition, health, social protection, environment, land management) and across rural, urban and peri-urban territories. As population growth and climate change put increasing strains on food systems and influence the flows of migrants along the rural-urban interface, implementing territorial planning that accounts for these changing dynamics is imperative. Intermediary cities would greatly benefit from territorial planning that takes into account their urbanisation process – in terms of both population and built-up expansion – and that addresses the food and nutrition security needs of urban and surrounding rural dwellers. As cities expand, arable agricultural land is increasingly being used for residential or industrial buildings, exacerbating risks of food insecurity and increasing displacement in developing countries (Cabannes Yves and Maricchino, 2018[186]).
Integrating food systems into territorial planning is an essential tool to ensure the food security of rural and urban dwellers. This implies ensuring affordability and facilitating access to nutritious food, especially for the most vulnerable (Cabannes Yves and Maricchino, 2018[186]), and fostering stronger and mutually beneficial linkages between rural and urban areas. This is particularly pertinent to intermediary cities, as they tend to account for a larger share of the urban poor than capital or metropolitan cities (Roberts and Hohmann, 2014[25]). In practice, integrating food systems into territorial planning implies promoting food production in local and surrounding areas and connecting producers in peri-urban and rural areas to urban markets through infrastructure and improved access to market services. This will promote the building of short supply chains and reduce food transportation needs and associated GHG emissions (Cabannes Yves and Maricchino, 2018[186]; Taguchi and Santini, 2018[187]). Short supply chains help to reduce food waste and water use as well as facilitating better access to nutrient food for local populations (Cabannes Yves and Maricchino, 2018[186]). They also help to strengthen rural-urban linkages and to improve food safety and the quality of nutrition. In order to enable land preservation around cities for agricultural production and food supply chains, intermediary cities also need to integrate land-use planning into their spatial and territorial planning that accounts for quickly transforming food systems, changes in food demand patterns and population growth (Cabannes Yves and Maricchino, 2018[186]).
An example of a programme for integrating food systems into spatial planning is the sustainable City Region Food System (CRFS) initiative, undertaken in 2015 by the FAO and RUAF (2015[188]). The programme promotes the need to go beyond city limits and integrate all elements of food systems into spatial planning. In Medellín (Colombia), one of seven cities to take part, the project had two main objectives: to strengthen and enhance regional value chains across the districts of Medellín and to improve the accessibility and availability of safe and varied food products to local populations (Dubbeling et al., 2017[189]). The programme led to the establishment of an inter-institutional taskforce, Buen Vivir.
Spatial plans must consider the needs of urban dwellers in informal settlements
Intermediary cities ought to address the needs of urban dwellers living in informal settlements. Growth in rural-to-urban migration has been one of the main causes of the expansion of informal settlements and precarious suburbanisation (Roberts and Hohmann, 2014[25]; Williams et al., 2019[106]; Satterthwaite et al., 2020[190]). Fast urbanisation will be conducive to the expansion of informal settlements unless effective planning is put in place. However, rural-to-urban migration (including seasonal and circular migration) is a critical aspect of livelihood diversification and poverty reduction strategies for urban and rural dwellers. As such, local, regional and national governments ought to implement policy measures that facilitate safe migration processes and invest in infrastructure and services that will contribute to the sustainable development of the rural-urban interface. For instance, local governments can invest in slum upgrading in areas that are not predisposed to environmental risk such as landslides or rising sea levels (Satterthwaite et al., 2020[190]). However, this requires generating information on population size, infrastructure needs and projected growth of demand within informal settlements. It is crucial to assess existing infrastructure stability and infrastructure needs (including waste management and water). Local governments ought to conduct surveys or collect other data to understand the needs of informal settlements and inform policy.
Spatial planning must include economic development and better access to services
Fostering inclusive and resilient economic development in rural and urban areas can strengthen local resilience to climate change. Well-managed economic development processes can help build adaptation capacities by contributing to improved human capital, increased income per capita and economic diversification, stronger institutions, local organisations, producer organisations, resilient infrastructure, etc. (Bowen, Cochrane and Fankhauser, 2012[191]; World Bank, 2010[192]). Fostering local economic development is a cost-effective way of strengthening local adaptation (World Bank, 2010[192]), as it can enable local governments to invest in climate-resilient infrastructure and locate development of assets or key infrastructure away from vulnerable areas (OECD, 2016[193]), as well as protecting local livelihoods from climate-induced losses (World Bank, 2010[192]).
The growth of intermediary cities presents opportunities for fostering local economic development and climate resilience. In some regions, especially in Southeast Asia, intermediary cities function as export processing zones, special economic zones or growth poles (Roberts and Hohmann, 2014[25]). Enabling rural-to-urban migration can make urban centres more dynamic with the addition of skilled and unskilled labour that can be absorbed into local economic activities. Establishing tailored policies that promote intermediary cities as poles for green growth and poverty reduction can help foster local and national economic growth and improve climate resilience. This was the case in Rwanda, where six intermediary cities – and the rural areas connected to them – were selected as poles of growth and poverty reduction, in line with the National Green Growth and Climate Resilient Strategy, established in 2011 (Republic of Rwanda, 2011[194]).
Tapping into the potential of food systems for local development and job creation
The ongoing transformation of food systems in developing countries can provide opportunities to integrate (rural) youth and rural migrants into the midstream and downstream sections of the food economy. Local governments can establish initiatives or regulations to incentivise enterprises and supermarkets working along food supply chains to create employment (Hussein and Suttie, 2016[36]). Labour-intensive value chains, such as horticulture, could provide opportunities for wage employment for rural youth (IFAD, 2014[195]; OECD, 2018[35]). The development of agro-industries and the growth in post-farm-gate activities (which often take place in small and medium-sized cities) can present attractive opportunities for rural youth. They also create an opportunity to provide education and training services, with more managerial and business services emerging along with the development of agro-business industries (OECD, 2018[35]). This can lead to mutually beneficial gains: rural youth can gain better access to income diversification mechanisms, while intermediary cities can experience growth in key areas and ensure the resilience of the food economy (Hussein and Suttie, 2016[36]) .
Addressing the needs of informal workers in food systems is integral for building resilient local economic development. As highlighted in this chapter, informality is prevalent in food systems, especially in African intermediary cities, and exposes informal workers to climate-related vulnerabilities. Despite the important role of the informal sectors in food systems – not only as key sources of livelihoods but also as key providers of food, particularly to poor populations in urban and rural areas – they often face bias from national and local policy makers and lack adequate support. For instance, in response to the COVID-19 pandemic, the South African government shut down informal open-air markets to reduce COVID transmission rates. However, this was highly detrimental to the poor urban population, who were reliant on informal markets and lacked the resources to buy large quantities of food from formal retail outlets (i.e. supermarkets) (Webb et al., 2021[196]). As such, local governments need to recognise the important role of informal traders or networks of activities for local food supplies, gain better understanding of their disproportionate vulnerabilities to climate-induced disruptions and provide support mechanisms to enhance their resilience and protect and strengthen livelihoods. Targeted policies including allocation of land-use permits, trading space and other key infrastructure, such as water, sanitation and waste management systems, are important actions to support livelihoods (Warren, 2018[197]).
The process of recovery from COVID-19 could serve as window of opportunity to support informal workers in intermediary cities. Unless appropriate policies are implemented, the economic and sanitary crisis brought on by the pandemic will exacerbate the challenges faced by informal workers (including migrants) and enterprises in intermediary cities. Roberts et al. (2020[198]) argue that the recovery from the pandemic could present opportunities to address and improve the informal economy. Strategies could include creating adequate spaces for informal businesses, helping them to integrate with regional markets and supply chains, and taking them into account in regulatory frameworks (Cities Alliance, 2020[199]).
Better access to urban services will strengthen the resilience of rural populations
Connecting rural populations to urban services is key to strengthening their long-term resilience and adaptation capacities. Better access to urban markets can enable rural producers to: increase their earnings and invest in more diversified economic activities (Hussein and Suttie, 2016[36]); access agricultural inputs and technology; better integrate into food supply chains; and reduce their climate vulnerabilities (FAO, 2019[90]). Intermediary cities are uniquely placed to do all this due to their close links to rural areas and their role as service providers and administrative centres for surrounding areas (Roberts and Hohmann, 2014[25]). Intermediary cities already facilitate access to agricultural extension services (Roberts and Hohmann, 2014[25]). This is important, as agriculture is and will be increasingly reliant on the services located in intermediary cities for both inputs and output markets (Berdegué et al., 2014[3]). As such, intermediary cities have large scope to help strengthen the resilience of rural livelihoods, by helping to build resilient production systems and by providing access to income-earning opportunities.
Intermediary cities can leverage their relations with rural areas to function as centres for marketing and processing, and to provide financial, advisory and knowledge services, all of which are needed to improve on-farm adaptation and mitigation strategies. This may be accomplished via government-run services or partnerships with the private sector, which could provide subsidised inputs. These services could be enhanced to integrate climate-resilience strategies such as climate-smart agriculture, intensification practices or crop/livestock rotation strategies (FAO, 2016[122]). Linking rural populations to urban services will lead to mutual benefits. Building stronger rural livelihoods can boost long-term resilience and reduce climate-induced migration to urban areas, reducing the pressure on urban services (Hussein and Suttie, 2016[36]). At the same time, improved livelihoods and incomes in rural areas will enable intermediary cities to benefit from larger markets for their goods and services.
Improving access to services such as education, training, transportation and communication is key for building urban and rural resilience. Provision of these services will also facilitate better integration into urban economies, such as better employment prospects, for future rural migrants. Access to communication and transportation also enables access to information regarding the employment or business opportunities available to rural dwellers (Hussein and Suttie, 2016[36]).
Integrating rural migrants into the local economy is a critical aspect of building local resilience. Rural migrants are highly vulnerable to climate change (as well as other systemic challenges, such as sanitary crises) in urban areas. Establishing migrant support systems, such as provision of work permits or health and education services, can help rural migrants both to address the immediate challenges they may face and to build resilience. This is particularly relevant for the large share of rural migrants who will likely be absorbed into the informal sector, especially those who are considered unskilled (Cattaneo et al., 2019[125]; Forefight, 2011[101]). Social protection services such as subsidised or low-cost health and education are important since migrants in informal settlements can face elevated health risks (Satterthwaite et al., 2020[190]; Bolay, 2020[104]). Investing in the human capital of rural migrants and integrating them into the local economy can help to strengthen their capacities to respond to climate events (World Bank, 2010[192]).
National governments play very important roles in the provision of social protection plans and safety nets for rural populations. Such services are largely beyond the mandates and administrative capacities of intermediary cities, whereas national or regional governments have more political capital and financial capacities to improve resilience in rural areas and to build stronger rural-urban linkages for long-term resilience. National or regional governments can implement policy actions that aim to strengthen rural livelihoods, through on-farm and off-farm strategies that build rural resilience in the face of fast- and slow-onset climate events. In Pakistan, for instance, social protection policies implemented by the national government, in addition to robust flood relief efforts, helped to manage the magnitude of the displacement caused by flooding events between 1991 and 2012 (Mueller, Gray and Kosec, 2014[200]; Cattaneo et al., 2019[125]).
Advance planning is needed to reduce the impact of climate disasters
Establishing proactive and long-term climate protocols can help to reduce mismanagement and facilitate effective recovery in climate disasters. As climate change weakens linkages and disrupts livelihoods across rural and urban areas, it will increase the vulnerability of local populations and weaken the consumption, production and livelihood linkages that connect intermediary cities with rural areas. This will increase strains in the provision of basic services in intermediary cities, such transportation, water and waste management. Protocols and action plans at the local level that reduce the potential impact of climate-induced disasters should focus on avoiding large economic losses and reducing local vulnerabilities.
Local governments, alongside national governments, need to strengthen disaster risk-management, rehabilitation and recovery systems. After climate disasters, it is a regular practice to conduct post-disaster assessments to review damage. Preparedness in the face of these events is crucial to ensure that climate disasters do not jeopardise the area’s development. Although the involvement of the national government is essential to unlock resources, implementation should be delegated to local governments to ensure a quick response. This will also facilitate better assistance to key areas and sectors, as local governments are best positioned to understand a city’s priorities (GFDRR, 2015[201]).
Planning emergency strategies to respond to climate disasters can reduce their negative impacts on well-being. Intermediate cities can play a special role in providing post-disaster services. To reduce the vulnerability of internally displaced people, they could provide access to safe housing in urban or peri-urban areas. Well-managed and well-placed safe housing, instead of makeshift housing, can reduce congestion and allow locals to continue to operate businesses, many of which are informal and rely on public spaces (Roberts and Hohmann, 2014[25]). Other strategies for managing climate risks, such as the use of technology for early-warning systems, early intervention systems (particularly in cases of slow-onset climate events, such as droughts), and social safety nets, have proven to be successful in reducing casualties and losses in countries including Mozambique, Niger, Kenya and Mauritania (European Commission, 2020[202]). Measures taken by the city of San Francisco, in the Philippines, provide an example of climate-disaster preparedness and are explained in more detail in Chapter 4.
Partnerships can help intermediary cities to strengthen their climate action
One challenge of implementing climate action lies in the fact that intermediary cities are highly heterogenous and there is no “one-size-fits-all” solution. For instance, as explained in Chapter 2, CO2 emissions and urban expansion follow different trends depending on the region and size of a city. As such, cities of 50 000 to 100 000 inhabitants in India do not necessarily follow the same path as similar cities in Latin America, or even as bigger cities in the same country. Policies aimed at mitigating climate change should consider this heterogeneity and adapt policy formulas to fit the local context.
Building partnerships with national and international stakeholders can enable local governments to strengthen their climate actions. Partnerships can help local governments to address knowledge gaps regarding their vulnerabilities to climate change. International organisations have large scope for creating platforms for dialogue between national and local governments and the populations they represent, whether in urban centres or the rural areas connected to them. Similarly, strengthening relationships with networks of cities and municipalities (both nationally and internationally) can provide the means and the space for exchange of information and experiences, and overall mutual learning. For example, partnerships with knowledge-producing institutions, such as universities and research institutes, and with the private sector, can support the efforts of local governments to collect data (on climate, food systems, migration, etc.). Moreover, strong direct relations with international donors can enable local governments to access international funds (depending on their level of decentralisation and fiscal autonomy) and to build capacity to formulate and implement bankable climate projects.
Conclusion
Intermediary cities play a central role in the rural-urban interface. They are critical in food system supply chains, provide opportunities for livelihood diversification and serve as an important destination for internal migration. As climate change disrupts food systems, spurring rural-to-urban migration, intermediary cities will face ripple effects. They will have to provide jobs, infrastructure and services for a growing population while at the same time working on climate adaptation and mitigation. Given these complex linkages, forward planning is needed. But policies aiming to improve climate resilience will have limited outcomes if they do not take account of the inherent interdependence of rural areas and urban centres. Preparing for climate shocks that may overstrain cities and affect food security requires spatial planning, infrastructure and housing development, and wider provision of services. Local governments can tap into rapidly transforming food systems to increase job creation. By building national and international partnerships, local governments can strengthen their capacities and increase their resources.
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Annex 3.A. Additional figures and tables
Annex Table 3.A.1. Linear and quadratic effect of average temperature and average precipitation on population in cities
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
---|---|---|---|---|---|---|
Log of Population |
Log of Population |
Log of Population |
Log of Population |
Log of Population |
Log of Population |
|
Average temperature |
-0.204** |
-0.203* |
-0.200* |
-0.188 |
||
(-2.60) |
(-2.00) |
(-2.54) |
(-1.85) |
|||
50K-100K* Avg temp. |
-0.317*** |
-0.343** |
-0.309*** |
-0.325** |
||
(-3.50) |
(-2.84) |
(-3.41) |
(-2.70) |
|||
100K-500K* Avg temp |
-0.314*** |
-0.418*** |
-0.311*** |
-0.410*** |
||
(-3.54) |
(-3.48) |
(-3.49) |
(-3.42) |
|||
500K-1M* Avg temp |
-0.303* |
-0.386** |
-0.300* |
-0.379** |
||
(-2.46) |
(-2.69) |
(-2.47) |
(-2.67) |
|||
Average temperature^2 |
0.00668*** |
0.00863*** |
0.00686*** |
0.00912*** |
||
(3.63) |
(3.75) |
(3.73) |
(3.98) |
|||
50K-100K* Avg temp^2 |
0.0118*** |
0.00999*** |
0.0115*** |
0.00917** |
||
(4.94) |
(3.40) |
(4.81) |
(3.14) |
|||
100K-500K*Avg temp^2 |
0.00981*** |
0.0100*** |
0.00967*** |
0.00970** |
||
(4.15) |
(3.38) |
(4.09) |
(3.27) |
|||
500K-1M* Avg temp^2 |
0.00790* |
0.00815* |
0.00782* |
0.00793* |
||
(2.07) |
(2.21) |
(2.08) |
(2.17) |
|||
Average rain |
-0.000976 |
0.00184 |
0.00108 |
0.00443 |
||
(-0.38) |
(0.47) |
(0.41) |
(1.11) |
|||
50K-100K* Average rain |
0.0103** |
0.0115* |
0.00780* |
0.00796 |
||
(3.14) |
(2.23) |
(2.38) |
(1.55) |
|||
100K-500K* Average rain |
0.00415 |
0.00343 |
0.00199 |
0.00124 |
||
(1.43) |
(0.78) |
(0.68) |
(0.28) |
|||
500K-1M* Average rain |
0.00559 |
0.00259 |
0.00423 |
0.000284 |
||
(1.06) |
(0.48) |
(0.81) |
(0.05) |
|||
Average rain^2 |
0.00000249 |
-0.00000800 |
-0.00000377 |
-0.0000150 |
||
(0.34) |
(-0.72) |
(-0.50) |
(-1.33) |
|||
50K-100K* Average rain^2 |
-0.0000261** |
-0.0000166 |
-0.0000219* |
-0.00000582 |
||
(-2.78) |
(-1.13) |
(-2.33) |
(-0.40) |
|||
100K-500K* Average rain^2 |
-0.00000930 |
0.000000382 |
-0.00000487 |
0.00000894 |
||
(-1.15) |
(0.03) |
(-0.60) |
(0.74) |
|||
500K-1M* Average rain^2 |
-0.0000154 |
0.00000167 |
-0.0000123 |
0.0000111 |
||
(-1.10) |
(0.12) |
(-0.85) |
(0.75) |
|||
Constant |
13.54*** |
14.23*** |
10.81*** |
10.53*** |
13.00*** |
12.98*** |
(42.81) |
(24.91) |
(143.47) |
(86.98) |
(37.47) |
(20.81) |
|
City FE |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Year FE |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
No. Observations |
34934 |
34934 |
34934 |
34934 |
34934 |
34934 |
N of Groups |
11681 |
11681 |
11681 |
11681 |
11681 |
11681 |
R-sq Within |
0.203 |
0.196 |
0.190 |
0.190 |
0.204 |
0.197 |
R-sq Between |
0.0294 |
0.0147 |
0.241 |
0.196 |
0.0539 |
0.0400 |
R-sq Overall |
0.0134 |
0.00596 |
0.0501 |
0.0767 |
0.0299 |
0.0232 |
Note: Average temperature measured in degrees Celsius and average precipitation measured in mm. Columns 1,3, 5 include average temperature and average rainfall calculated over the past 5 years. Columns 2,4,6 include average temperature and average rainfall calculated over the past 10 years. Estimates of a panel econometric model regressing population of cities on: average temperature and its quadratic term (identified by ^2), and average rainfall and its quadratic term (identified by ^2). Variables interacted with city size. Base category is cities above 1 million inhabitants. Dependent variable expressed in logarithmic terms. Results based on a fixed-effects estimator and standard errors clustered by city. Year fixed effects are included. Data for the periods 1990, 2000 and 2015. t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.
Source: Author’s own calculations using GHS Urban Centre Database (2019[166]).
Annex Table 3.A.2. Main effects of linear and quadratic effect of average temperature and average precipitation by city size, on population in cities
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
---|---|---|---|---|---|---|
Log of Population |
Log of Population |
Log of Population |
Log of Population |
Log of Population |
Log of Population |
|
Average temperature |
||||||
50K-100K |
-0.520*** |
-0.546*** |
-0.508*** |
-0.513*** |
||
(-11.30) |
(-8.43) |
(-11.08) |
(-7.92) |
|||
100K-500K |
-0.518*** |
-0.621*** |
-0.511*** |
-0.598*** |
||
(-12.21) |
(-9.71) |
(-11.96) |
(-9.35) |
|||
500K-1M |
-0.507*** |
-0.589*** |
-0.499*** |
-0.566*** |
||
(-5.28) |
(-5.72) |
(-5.35) |
(-5.60) |
|||
>1M |
-0.204** |
-0.203* |
-0.200* |
-0.188 |
||
(-2.60) |
(-2.00) |
(-2.54) |
(-1.85) |
|||
Average temperature^2 |
||||||
50K-100K |
0.0185*** |
0.0186*** |
0.0183*** |
0.0183*** |
||
(11.77) |
(9.39) |
(11.70) |
(9.27) |
|||
100K-500K |
0.0165*** |
0.0187*** |
0.0165*** |
0.0188*** |
||
(10.78) |
(9.11) |
(10.77) |
(9.19) |
|||
500K-1M |
0.0146*** |
0.0168*** |
0.0147*** |
0.0170*** |
||
(4.34) |
(5.72) |
(4.46) |
(5.90) |
|||
>1M |
0.00668*** |
0.00863*** |
0.00686*** |
0.00912*** |
||
(3.63) |
(3.75) |
(3.73) |
(3.98) |
|||
Average rain |
||||||
50K-100K |
0.00932*** |
0.0134*** |
0.00888*** |
0.0124*** |
||
(4.61) |
(4.00) |
(4.47) |
(3.76) |
|||
100K-500K |
0.00318* |
0.00526** |
0.00307* |
0.00566** |
||
(2.29) |
(2.67) |
(2.26) |
(2.90) |
|||
500K-1M |
0.00461 |
0.00443 |
0.00531 |
0.00471 |
||
(1.00) |
(1.18) |
(1.17) |
(1.24) |
|||
>1M |
-0.000976 |
0.00184 |
0.00108 |
0.00443 |
||
(-0.38) |
(0.47) |
(0.41) |
(1.11) |
|||
Average rain^2 |
||||||
50K-100K |
-0.0000236*** |
-0.0000246** |
-0.0000257*** |
-0.0000208* |
||
(-4.10) |
(-2.58) |
(-4.42) |
(-2.22) |
|||
100K-500K |
-0.00000680* |
-0.00000762 |
-0.00000863** |
-0.00000607 |
||
(-2.11) |
(-1.70) |
(-2.59) |
(-1.39) |
|||
500K-1M |
-0.0000129 |
-0.00000633 |
-0.0000160 |
-0.00000391 |
||
(-1.09) |
(-0.69) |
(-1.29) |
(-0.41) |
|||
>1M |
0.00000249 |
-0.00000800 |
-0.00000377 |
-0.0000150 |
||
(0.34) |
(-0.72) |
(-0.50) |
(-1.33) |
|||
Observations |
34934 |
34934 |
34934 |
34934 |
34934 |
34934 |
Note: Average temperature measured in degrees Celsius and average precipitation measured in mm. Columns 1,3, 5 include average temperature and average rainfall calculated over the past 5 years. Columns 2,4,6 include average temperature and average rainfall calculated over the past 10 years. Estimates of a panel econometric model regressing population of cities on: average temperature and its quadratic term (identified by ^2), and average rainfall and its quadratic term (identified by ^2). Variables interacted with city size. Base category is cities above 1 million inhabitants. Dependent variable expressed in logarithmic terms. Results based on a fixed-effects estimator and standard errors clustered by city. Year fixed effects are included. Data for the periods 1990, 2000 and 2015. t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.
Source: Author’s own calculations using GHS Urban Centre Database (2019[166]).
Annex Table 3.A.3. Linear and quadratic effect of average temperature and average precipitation on urban share
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
---|---|---|---|---|---|---|
Log of % urban share |
Log of % urban share |
Log of % urban share |
Log of % urban share |
Log of % urban share |
Log of % urban share |
|
Average temperature |
0.0264 |
0.0315* |
0.0256 |
0.0316* |
||
(1.37) |
(2.39) |
(1.33) |
(2.40) |
|||
50K-100K* Avg temp. |
-0.0369 |
-0.0536*** |
-0.0373 |
-0.0528*** |
||
(-1.85) |
(-3.71) |
(-1.87) |
(-3.65) |
|||
100K-500K* Avg temp |
-0.0290 |
-0.0348** |
-0.0291 |
-0.0343* |
||
(-1.49) |
(-2.58) |
(-1.50) |
(-2.54) |
|||
500K-1M* Avg temp |
-0.0320 |
-0.0300* |
-0.0321 |
-0.0297* |
||
(-1.61) |
(-2.08) |
(-1.61) |
(-2.05) |
|||
Average temperature^2 |
-0.000270 |
-0.000259 |
-0.000264 |
-0.000239 |
||
(-0.79) |
(-1.02) |
(-0.78) |
(-0.94) |
|||
50K-100K* Avg temp^2 |
0.000545 |
0.000862** |
0.000568 |
0.000830** |
||
(1.52) |
(3.00) |
(1.58) |
(2.90) |
|||
100K-500K*Avg temp^2 |
0.000470 |
0.000589* |
0.000478 |
0.000570* |
||
(1.35) |
(2.23) |
(1.38) |
(2.17) |
|||
500K-1M* Avg temp^2 |
0.000606 |
0.000587 |
0.000609 |
0.000573 |
||
(1.64) |
(1.96) |
(1.65) |
(1.92) |
|||
Average rain |
-0.000164 |
0.000160 |
-0.000113 |
0.000206 |
||
(-1.00) |
(0.49) |
(-0.70) |
(0.63) |
|||
50K-100K* Average rain |
-0.0000243 |
0.000172 |
-0.0000620 |
0.000145 |
||
(-0.13) |
(0.48) |
(-0.32) |
(0.40) |
|||
100K-500K* Average rain |
0.000182 |
0.00000943 |
0.000119 |
-0.0000292 |
||
(0.96) |
(0.03) |
(0.63) |
(-0.08) |
|||
500K-1M* Average rain |
0.0000275 |
-0.000266 |
-0.00000188 |
-0.000233 |
||
(0.12) |
(-0.69) |
(-0.01) |
(-0.61) |
|||
Average rain^2 |
0.000000138 |
-0.000000794 |
-0.000000106 |
-0.00000100 |
||
(0.31) |
(-0.82) |
(-0.24) |
(-1.06) |
|||
50K-100K* Average rain^2 |
-0.000000473 |
0.000000134 |
-0.000000243 |
0.000000359 |
||
(-0.86) |
(0.12) |
(-0.45) |
(0.34) |
|||
100K-500K* Average rain^2 |
-0.000000630 |
0.000000470 |
-0.000000404 |
0.000000719 |
||
(-1.21) |
(0.45) |
(-0.79) |
(0.70) |
|||
500K-1M* Average rain^2 |
-9.63e-08 |
0.00000127 |
-5.68e-09 |
0.00000139 |
||
(-0.14) |
(1.11) |
(-0.01) |
(1.26) |
|||
Constant |
-0.142*** |
-0.143*** |
-0.129*** |
-0.156*** |
-0.119*** |
-0.173*** |
(-4.56) |
(-3.76) |
(-31.15) |
(-24.69) |
(-3.72) |
(-4.56) |
|
City FE |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Year FE |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
No. Observations |
26503 |
26503 |
26503 |
26503 |
26503 |
26503 |
N of Groups |
8838 |
8838 |
8838 |
8838 |
8838 |
8838 |
R-sq Within |
0.137 |
0.138 |
0.137 |
0.135 |
0.139 |
0.138 |
R-sq Between |
0.0137 |
0.0172 |
0.000139 |
0.0000283 |
0.0123 |
0.0201 |
R-sq Overall |
0.0145 |
0.0176 |
0.00207 |
0.00149 |
0.0131 |
0.0205 |
t statistics in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
Note: Average temperature measured in degrees Celsius and average precipitation measured in mm. Columns 1,3, 5 include average temperature and average rainfall calculated over the past 5 years. Columns 2,4,6 include average temperature and average rainfall calculated over the past 10 years. Estimates of a panel econometric model regressing urban share (% of people of the functional urban area living in the high-density centre) on: average temperature and its quadratic term (identified by ^2), and average rainfall and its quadratic term (identified by ^2), Variables interacted with city size. Base category is cities above 1 million inhabitants. Dependent variable expressed in logarithmic terms. Results based on a fixed-effects estimator and standard errors clustered by city. Year fixed effects are included. Data for the periods 1990, 2000 and 2015.
Source: Author’s own calculations using GHS Urban Centre Database (2019[166]).
Annex Table 3.A.4. Main effects of linear and quadratic effect of average temperature and average precipitation by city size, on urban share
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
---|---|---|---|---|---|---|
Log of % Urban in FUA |
Log of % Urban in FUA |
Log of % Urban in FUA |
Log of % Urban in FUA |
Log of % Urban in FUA |
Log of % Urban in FUA |
|
Average temperature |
||||||
50K-100K |
-0.0105* |
-0.0221*** |
-0.0117* |
-0.0212*** |
||
(-2.09) |
(-3.69) |
(-2.31) |
(-3.55) |
|||
100K-500K |
-0.00261 |
-0.00326 |
-0.00349 |
-0.00267 |
||
(-1.12) |
(-1.10) |
(-1.50) |
(-0.90) |
|||
500K-1M |
-0.00557 |
0.00149 |
-0.00645 |
0.00193 |
||
(-1.14) |
(0.25) |
(-1.30) |
(0.32) |
|||
>1M |
0.0264 |
0.0315* |
0.0256 |
0.0316* |
||
(1.37) |
(2.39) |
(1.33) |
(2.40) |
|||
Average temperature^2 |
||||||
50K-100K |
0.000275* |
0.000603*** |
0.000304** |
0.000591*** |
||
(2.43) |
(4.56) |
(2.67) |
(4.48) |
|||
100K-500K |
0.000199** |
0.000331*** |
0.000213*** |
0.000331*** |
||
(3.15) |
(4.63) |
(3.35) |
(4.62) |
|||
500K-1M |
0.000335* |
0.000328* |
0.000344* |
0.000334* |
||
(2.41) |
(2.06) |
(2.46) |
(2.10) |
|||
>1M |
-0.000270 |
-0.000259 |
-0.000264 |
-0.000239 |
||
(-0.79) |
(-1.02) |
(-0.78) |
(-0.94) |
|||
Average rain |
||||||
50K-100K |
-0.000188 |
0.000331* |
-0.000175 |
0.000351* |
||
(-1.74) |
(2.06) |
(-1.59) |
(2.20) |
|||
100K-500K |
0.0000179 |
0.000169 |
0.00000573 |
0.000177 |
||
(0.19) |
(1.16) |
(0.06) |
(1.24) |
|||
500K-1M |
-0.000136 |
-0.000107 |
-0.000115 |
-0.0000264 |
||
(-0.81) |
(-0.51) |
(-0.70) |
(-0.13) |
|||
>1M |
-0.000164 |
0.000160 |
-0.000113 |
0.000206 |
||
(-1.00) |
(0.49) |
(-0.70) |
(0.63) |
|||
Average rain^2 |
||||||
50K-100K |
-0.000000334 |
-0.000000661 |
-0.000000349 |
-0.000000642 |
||
(-1.01) |
(-1.35) |
(-1.04) |
(-1.32) |
|||
100K-500K |
-0.000000492 |
-0.000000324 |
-0.000000510 |
-0.000000281 |
||
(-1.79) |
(-0.80) |
(-1.86) |
(-0.70) |
|||
500K-1M |
4.19e-08 |
0.000000475 |
-0.000000112 |
0.000000385 |
||
(0.08) |
(0.79) |
(-0.23) |
(0.67) |
|||
>1M |
0.000000138 |
-0.000000794 |
-0.000000106 |
-0.00000100 |
||
(0.31) |
(-0.82) |
(-0.24) |
(-1.06) |
|||
Observations |
26503 |
26503 |
26503 |
26503 |
26503 |
26503 |
Note: Average temperature measured in degrees Celsius and average precipitation measured in mm. Columns 1, 3, and 5 include average temperature and average rainfall calculated over the past 5 years. Columns 2, 4, and 6 include average temperature and average rainfall calculated over the past 10 years. Estimates of a panel econometric model regressing urban share (% of people of the functional urban area living in the high-density centre) on: average temperature and its quadratic term (identified by ^2), and average rainfall and its quadratic term (identified by ^2), Variables interacted with city size. Base category is cities above 1 million inhabitants. Dependent variable expressed in logarithmic terms. Results based on a fixed-effects estimator and standard errors clustered by city. Year fixed effects are included. Data for the periods 1990, 2000 and 2015.
Source: Author’s own calculations using GHS Urban Centre Database (2019[166]).
Notes
← 1. The countries of the study include: Côte d’Ivoire, Ghana, Mali, Namibia, Niger, Nigeria, Senegal, South Africa, Tanzania, Uganda and Zambia.
← 2. Farms smaller than two hectares.
← 3. The analysis by Zavesi (2021; 54) focuses on the “impact of repeated water shocks (e.g. decline in rainfall) that occur over a decade on out-migration rates”.
← 4. This result contradicts that of Castells-Quintana et al. (2021[165]), which finds that less precipitation leads to higher population in dry places. Nevertheless, the difference might lie in the use of a different sample, as this analysis only considers developing countries.