Firewood, forests, and fringe populations: Exploring the inequitable socioeconomic dimensions of Liquified Petroleum Gas (LPG) adoption in India

Willingness to pay for cooking with electricity among rural households: The case of Southern Ethiopia

One way to fulfill the worldwide goal of clean energy outlined in SDG 7 is by adopting modern and alternative energy sources, specifically through electric cooking. Most rural households in developing countries, however, lack access to a clean and affordable energy source; thus, a significant part of the population relies on solid fuels. This study investigates the factors influencing households' willingness to pay for electricity services for cooking in rural areas of Southern Ethiopia, where access to clean and affordable energy is limited. A multi-stage cluster sampling design was used to select 571 households from six randomly chosen woredas (districts) in the Southern Nations, Nationalities, and Peoples region. The results show that 45.53 percent of the respondents were willing to pay for electricity for cooking, with a mean WTP of 510.12 Birr, representing a 13.36 percent increase over the current average bill rate. Factors such as total family income, distance from the main road, type of cooking stove, and the number of power outages per day significantly influence households' willingness to pay for electricity for cooking. Among other reforms, the electricity authority should improve the quality of electric service and access in rural areas. Given the relationship between the WTP for cooking with electricity and household income, it would benefit the government to consider providing subsidies for electricity supply to households with lower incomes.

Household unclean fuel use, indoor pollution and self-rated health: risk assessment of environmental pollution caused by energy poverty from a public health perspective

The lack of access to clean energy remains one of the major challenges in the global energy sector. Access to clean, sustainable and affordable energy, outlined in the seventh Sustainable Development Goals (SDG 7) of the United Nations, plays a crucial role in advancing health (SDG 3), as unclean cooking energy may endanger people's health by causing air pollution. However, due to endogeneity problems such as reverse causality, the health consequences of environmental pollution caused by unclean fuel usage are difficult to be scientifically and accurately evaluated. This paper aims to systematically assess the health cost of unclean fuel usage based on tackling endogeneity, using the data from Chinese General Social Survey. The ordinary least squares model, ordered regression methods, instrumental variable approach, penalized machine learning methods, placebo test, and mediation models are applied in this research. Analytical results demonstrate that households' unclean fuel use significantly damages people's health. Specifically, the use of dirty fuel leads to an average of about a one-standard-deviation decline in self-rated health, demonstrating its notable negative effect. The findings are robust to a series of robustness and endogeneity tests. The impact mechanism is that unclean fuel usage reduces people's self-rated health through increasing indoor pollution. Meanwhile, the negative effect of dirty fuel use on health has significant heterogeneity among different subgroups. The consequences are more prominent for the vulnerable groups who are female, younger, living in rural areas and older buildings, with lower socio-economic status and uncovered by social security. Therefore, necessary measures should be taken to improve energy infrastructure to make clean cooking energy more affordable and accessible as well as to enhance people's health. Besides, more attention should be paid to the energy needs of the above specific vulnerable groups faced with energy poverty.

Prediction of consumers refill frequency of LPG: A study using explainable machine learning

Launched in 2016, the PMUY Programme of the Government of India aimed to provide 8 crore LPG connections to women in rural households over four years. After acquiring a new connection, some households appeared uninterested in ordering subsequent subsidized LPG refills, impacting programme's sustainability, and targeting strategy. We propose a prediction model using "Explainable Machine Learning" to anticipate the beneficiaries' refill frequency with a view to improving LPG-refills and social targeting. In this paper, we suggest an enhanced stacked SVM (ISS) model for classification, which is contrasted with state-of-art ML models: Random Forest (RF), SVM-RBF, Naive Bayes (NB), and Decision Tree (C5.0). Some of the performance matrices that are used to evaluate the models include accuracy, sensitivity, specificity, Cohen's Kappa statistics, Receiver Operating Characteristic curve (ROC), and area under the curve (AUC). The proposed approach, which was validated with 10-fold cross validation, produced the best overall accuracies for data splits of 50-50, 66-34, and 80-20. The "Explainable AI (XAI)" model has also been used to describe how models and features interact, and to discuss the importance of features and their contributions to prediction. The recommended XAI will aid in efficient "beneficiary targeting" and "policy interventions".

Land cover and forest health indicator datasets for central India using very-high resolution satellite data

Satellite imagery has been used to provide global and regional estimates of forest cover. Despite increased availability and accessibility of satellite data, approaches for detecting forest degradation have been limited. We produce a very-high resolution 3-meter (m) land cover dataset and develop a normalized index, the Bare Ground Index (BGI), to detect and map exposed bare ground within forests at 90 m resolution in central India. Tree cover and bare ground was identified from Planet Labs Very High-Resolution satellite data using a Random Forest classifier, resulting in a thematic land cover map with 83.00% overall accuracy (95% confidence interval: 61.25%-90.29%). The BGI is a ratio of bare ground to tree cover and was derived by aggregating the land cover. Results from field data indicate that the BGI serves as a proxy for intensity of forest use although open areas occur naturally. The BGI is an indicator of forest health and a baseline to monitor future changes to a tropical dry forest landscape at an unprecedented spatial scale.

Equity, technological innovation and sustainable behaviour in a low-carbon future

The world must ambitiously curtail greenhouse gas emissions to achieve climate stability. The literature often supposes that a low-carbon future will depend on a mix of technological innovation-improving the performance of new technologies and systems-as well as more sustainable behaviours such as travelling less or reducing waste. To what extent are low-carbon technologies, and their associated behaviours, currently equitable, and what are potential policy and research implications moving forward? In this Review, we examine how four innovations in technology and behaviour-improved cookstoves and heating, battery electric vehicles, household solar panels and food-sharing-create complications and force trade-offs on different equity dimensions. We draw from these cases to discuss a typology of inequity cutting across demographic (for example, gender, race and class), spatial (for example, urban and rural divides), interspecies (for example, human and non-human) and temporal (for example, future generations) vulnerabilities. Ultimately, the risk of inequity abounds in decarbonization pathways. Moreover, low-carbon innovations are not automatically just, equitable or even green. We show how such technologies and behaviours can both introduce new inequalities and reaffirm existing ones. We then discuss potential policy insights and leverage points to make future interventions more equitable and propose an integrated research agenda to supplement these policy efforts.