Editing IPCC:AR6/WGII/Chapter-12 (section)

=== FAQ 12.1 | How are inequality and poverty limiting options to adapt to climate change in Central and South America? ===

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''Poverty and inequality decrease human capacity to adapt to climate change. Limited access to resources may reduce the ability of individuals, households and societies to adapt to the impacts of climate change and variability because of the narrow response portfolio. Inequality limits responses available to vulnerable segments as most adaptation options are resource-dependent.''

Though poverty in Central and South America has decreased over the last 12 years, inequality remains as a historic and structural characteristic of the region. In 2018, 29.5% of Latin America’s population (including Mexico) were poor (182 million) and 10.2% were extremely poor (63 million), more than half of them living in urban areas. In 2020, due to COVID crisis Gini coefficient projection of increases is ranging from 1.1% to 7.8%, poverty increased to 33.7% (209 millions) and extreme poverty to 12.5% (78 millions).

Poor populations have little or no access to good-quality education, information, health systems and financial services. They have fewer chances to access resources, such as land and water, good-quality housing, risk-reducing infrastructure, and services, such as running water, sanitation and drainage. Their lack of political clout and endowments limits their access to assets for withstanding and recovering from shocks and stresses. Poverty, inequality and high vulnerability to the impacts of climate change are interrelated processes. Poor populations are highly vulnerable to the impacts of climate change and are usually located in areas of high exposure to extreme events. The constant loss of assets and livelihoods in both urban and rural areas drives communities into chronic poverty traps, exacerbating local poverty cycles and creating new ones.

For instance, climate-related reduced yields in crops, fisheries and aquaculture have a substantial impact on the livelihoods and food security of families and affect their options for coping with and adapting to climate change and variability. The impact of climate change in agriculture for CSA depends on determinants such as the availability of natural resources, access to markets, diversity of inputs and production methods, quality and coverage of infrastructure and socioeconomic characteristics of the population. Impacts from climate change on small-scale farmers compromise the livelihoods and food security of rural areas and, consequently, the food supply for urban areas.

Governments in the region have implemented several poverty-reduction programmes. However, policies of income redistribution and poverty alleviation do not necessarily improve climate risk management, so complementary policies integrating both social and material conditions are required. A study in northern Brazil showed that risk management strategies for droughts and food insecurity did not change poverty rates between 1997–1998 and 2011–2012. Major shocks, such as climate and extreme weather events (e.g., floods, heavy rains, droughts, frost), reduce and destroy public and private property. For instance, the ENSO event of 2017 in Peru caused losses estimated between USD 6 and 9 billion, affected more than a million inhabitants and generated 370,000 new poor. In total, losses by unemployment, deaths, destruction and damage to infrastructure and houses were around 1.3% of the GDP of Peru.

Low government spending on social infrastructure (e.g., health, education), ethnic discrimination and social exclusion reduce healthcare access, leaving poor people in entire regions mostly undiagnosed or untreated. In a context of privatisation policies of healthcare systems, research shows that marginal people lack identifying documents needed to access public services in Buenos Aires (Argentina), Mexico City (Mexico) and Santiago de Chile (Chile), some of the most developed cities in the region. The consequences of this situation are underreporting, low diagnosis and low treatment of diseases such as vector-borne diseases such as dengue and risk of diarrhoeal diseases originating from frequent flooding in Amazonian riverine communities. Bias in reporting on access to healthcare and the incidence of diseases in marginal populations is usually region-dependent. For example, in Brazil’s Amazonian north in 2018, there were 2.2 medical doctors per 1000 inhabitants, while 4.95 medical doctors per 1000 inhabitants and 9.52 doctors in São Paulo and Santa Catarina respectively. Another example is pregnant women in remote Amazonian municipalities, who receive less prenatal care than women in urban areas. These social inequities underlie systemic biases in health data quality, hindering reliable estimation of disease burdens such as the distribution of disease or birth and death registrations. For example, in Guatemala, alternative Indigenous healthcare systems are responding to local needs in Mayan communities. However, this remains unrecognised. The existence of health institutions based on IK can reinforce the lack of universal coverage by central government healthcare, addressing the miscalculation of morbidity, mortality and cause of death among disadvantaged groups.

Inequality, informality and precariousness are particularly relevant barriers to adaptation. A significant part of the construction sector in the region is informal and does not follow regulations for land use and construction safety codes, and there is a lack of public strategies for housing access. Adaptive construction is based on up-to-date regulation and codes, appropriate design and materials, and access to infrastructure and services. Decreasing inequality and eradicating poverty are crucial for achieving proper adaptation to climate change in the region. Some anti-poverty initiatives, such as savings groups, microfinance for improving housing or assets and community enterprises, may also support specific adaptive measures. These mechanisms should be widely accessible to poor groups and be complemented by comprehensive poverty alleviation programmes that include climate-change adaptation.

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