Beyond averages and aggregates

By Shonali Pachauri, Senior Researcher in the IIASA Energy Program

Shonali Pachauri explains why data, indicators, and monitoring at finer scales are important to ensure that everyone benefits from policies and efforts aimed at achieving national and global development goals.

A world where no one is left behind by 2030, is the promise nations have made by adopting the United Nations’ Agenda for Sustainable Development. But how does one ensure that no one is left behind? It requires designing inclusive policies and programs that target the most vulnerable and marginalized regions and populations. Sound data and indicators underpin our current understanding of the status of development and are an important part of periodic reviews to determine the direction and pace of progress towards achieving agreed goals. These form the basis of informed decisions and evidence-based policymaking. While an exhaustive list of indicators has been prescribed to monitor progress towards the globally agreed goals, these have been largely defined at a national scale. These goals rely overwhelmingly on simple averages and aggregates that mask underlying variations and distributions.

Indian woman walking home with fire wood © Devy | Dreamstime.com

Recent work I’ve been involved in makes the pitfalls of working with averages and aggregates alone abundantly clear. They can obscure uneven patterns of changes and impacts across regions and groups within the same nation. The overall conclusion of this work is that, even if the globally agreed goals are met by 2030, this is no guarantee that everyone will benefit from their achievement.

A recent Nature Energy – News & Views piece I was invited to write reports on a study that assessed the impacts of China’s recent coal to electricity program across villages in the Beijing municipal region. The program subsidizes electricity and electric heat pumps and has been rolling out a ban on coal use for household heating. The study found that the benefits of the program to home comfort, air quality, and wellbeing varied significantly across rich and poor districts. In poor districts, the study found that the ban was not effective as poor households were still unable to afford the more expensive electric heating and were continuing to rely on coal. Studies such as this one that help us understand how and why benefits of a program may vary across regions or population groups can aid policy- and decision makers in formulating more fair and inclusive policies.

In other recent research carried out with colleagues in the IIASA Energy Program, the Future Energy Program at the Fondazione Eni Enrico Mattei (FEEM) in Italy, and the Institute for Integrated Energy Systems at the University of Victoria, Canada, we developed a detailed satellite nightlights derived dataset to track progress with providing electricity access at a sub-national level in Africa. We found that while progress with electrification between 2014 and 2018 varied across nations, at a sub-national provincial level, disparities were even more pronounced. Even more surprising, while electricity access is generally higher and easier to extend in urban areas, we found urban pockets where access has stagnated or even worsened. This correlated with areas where in-migration of populations had been high. These areas likely include urban slums or peri-urban regions where expanding electricity access continues to be challenging. Furthermore, our analysis shows that even where access has been extended, there are regions where electricity use remains extremely low, which means that people are not really benefitting from the services electricity can provide.

In a final example, of research carried out with collaborators from the University of British Columbia and the Stockholm Environment Institute, we evaluated a large nationwide program to promote cooking with liquefied petroleum gas (LPG) in Indian households to induce a shift away from the use of polluting solid fuels. While this program specifically targets poor and deprived, largely rural households, our assessment found that although there has been an unprecedented increase in enrollments of new LPG customers under the program, this has not been matched by an equal increase in LPG sales. In fact, we found consumption of LPG by program beneficiaries was about half that of the average rural consumer. Moreover, when we examined how purchases were distributed across all new consumers, we found that about 35% of program beneficiaries purchased no refills during the first year and only 7% bought enough to substitute half or more of their total cooking energy needs with LPG. Clearly, the health and welfare benefits of a transition to cleaner cooking are still to be realized for most people covered by this program.

Analyses, such as the examples I’ve discussed here, clearly highlight that we need data, indicators, and monitoring at much finer scales to really assess if all regions and populations are benefitting from policies and efforts to achieve national and globally agreed development goals. Relying on aggregates and averages alone may paint a picture that hides more than it reveals. Thus, without such finer-scale analysis and an understanding of the distributional impacts of policies and programs, we may end up worsening inequalities and leaving many behind.

 References:

[1] Pachauri S (2019). Varying impacts of China’s coal ban. Nature Energy 4: 356-357. [pure.iiasa.ac.at/15905]

[2] Falchetta G, Pachauri S, Parkinson S, & Byers E (2019). A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Scientific Data 6 (1): art. 110. [pure.iiasa.ac.at/15982]

[3] Kar A, Pachauri S, Bailis R, & Zerriffi H (2019). Using sales data to assess cooking gas adoption and the impact of India’s Ujjwala program in rural Karnataka. Nature Energy [pure.iiasa.ac.at/15994]

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Exploring urban-rural differences in health risks from extreme temperatures

By Kejia Hu, PhD Candidate at Zhejiang University, China and IIASA Young Scientists Summer Program (YSSP) 2016 participant 

Kejia Hu, an alumna of the IIASA Young Scientists Summer Program, discusses a recent study on how extreme temperatures affect the health risks experienced by urban and rural communities in China. 

Hot and cold temperatures are associated with increased risks of cause-specific mortality, in other words, deaths that result from, for instance, cardiovascular and respiratory conditions. Due to the urban heat island effect – where an urban heat island is a city or metropolitan area that is significantly warmer than its surrounding rural areas due to human activity – it is commonly assumed that urban residents are at a higher risk of exposure to extreme heat than their rural neighbors are. Very few studies have explored the urban-rural differences of temperature-related health risks, often because of the lack of sufficient meteorological and health data in rural areas.

Today, 45% of the global population – nearly 3.4 billion people – still live in rural regions, and based on the UN’s World Urbanization Prospects 2018, there will still be more than 3 billion people living in these areas by 2050, despite current trends such as urbanization.

This made us wonder whether there could be a rural-urban gap in extreme temperature induced health risk. We decided to conduct a study to address this question in Zhejiang province in eastern China employing high spatial resolution data on temperature, death registrations, air pollution, and population density across 89 counties in Zhejiang from 2009 to 2015.

Based on an epidemiological analysis of more than 2 million death cases, we found that rural residents are more sensitive to both cold and hot temperatures than urban residents, and that extreme temperatures especially affect the elderly. Our results indicate that extreme cold temperatures increased the mortality rate by 98% for rural populations, and by 47% for urban populations, while extremely hot temperatures increased the mortality rate by 18% for rural populations and by 14% for urban populations. When considering both human exposure and vulnerability, the attributable deaths from cold and hot temperatures were 4.8 and 2.6 times higher in rural than in urban areas, respectively.

© Elwynn | Dreamstime.com

But why do rural populations tend to have higher mortality risks in the face of both cold and heat? Our results suggest that age, education, income, access to health care services and air conditioners, and the types of occupations that residents in these areas engage in, are among the potential sources. We found that counties with higher percentages of elderly and agricultural employment, lower levels of education, lower income levels, fewer hospital beds, and fewer air conditioners, had higher mortality risks related to both heat and cold. This could mean that socioeconomic vulnerability may play an important role – even more important than temperature in the determinants of temperature-related health risks.

Ours is the first study to find an urban–rural disparity in both heat and cold mortality risks. Importantly, it challenges the general assumption in previous studies in developed countries that urban residents are at a higher risk to extreme high temperatures. Our findings suggest that previous studies, which mostly investigated exposure-response associations using data from urban areas, may have underestimated the mortality burden for the entire population.

Although the Chinese government have standards in place regarding thermal comfort in residential buildings and highly recommend that they are implemented for rural houses, unfortunately, until now, this has only been enforced for urban apartments in China. In addition, due to lower income, rural households are more likely to fall into “fuel poverty” compared to urban households, which will limit the use of air conditioners for rural people. Targeted measures such as financial assistance for paying electricity bills will help build rural residents’ resilience to extreme temperatures.

Our findings have important implications for policy, particularly in developing countries. Overall, no single action will be enough to reduce the temperature-related mortality risks in rural areas. More efforts should be made to narrow the urban-rural gaps that persist in access to health care by, for example, increasing investment in health care facilities and health care professionals in rural areas. Improving rural people’s general awareness of temperature related risks, such as to popularize preventive knowledge and to develop early warning systems is also needed to prevent temperature-related deaths.

Reference:

Hu K, Guo Y, Hochrainer-Stigler S, Liu W, See L, Yang X, Zhong J, Fei F, et al. (2019). Evidence for Urban–Rural Disparity in Temperature–Mortality Relationships in Zhejiang Province, China. Environmental Health Perspectives 127 (3): e037001. [pure.iiasa.ac.at/15773]

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.