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Abstract Focusing on coal energy from a geographical perspective, the unintended regional consequences of coal mining and combustion in the Czech Republic are discussed and analysed in terms of the environmental injustice and resource curse theories. The explorative case study attempts to identify significant associations between the spatially uneven distribution of coal power plants and the environmental and socioeconomic characteristics and development trends of affected areas. The findings indicate that the coal industries have contributed to slightly above average incomes and pensions, and have provided households with some technical services such as district heating. However, these positive effects have come at high environmental and health costs paid by the local populations. Above average rates of unemployment, homelessness and crime indicate that the benefits have been unevenly distributed economically. A higher proportion of uneducated people and ethnic minorities in affected districts suggest that coal energy is environmentally unjust.

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

Abstract As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. Grasping the carbon emission reduction potential has a direct impact on the implementation of the carbon emission reduction policies of China. The paper builds a super-Slack Based Model(SBM) considering this undesirable output, and calculates the carbon emission efficiency. Then, the Meta-Frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyse the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed and the conclusions are as follows: there is a big difference in the carbon emission Technology Gap Ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing. The following is recommended: the government should rationally distribute energy-intensive industries, promote industrial structure adjustment, optimize the energy structure according to the regional industrial advantages; increase investment in R&D, promote energy technology innovation in energy-intensive industries; prioritize the promotion of carbon peaks on key emission industries and regional, formulate differentiated plans for the regions and industries with different carbon emission reduction potentials.

Abstract Renewable energy should play a crucial role in increasing energy supplies and achieving the potential target of reducing 50% of CO2 emissions by 2050. The main objective of this study is to propose a neuro-fuzzy modelling entitled ensemble-Adaptive Neuro-Fuzzy Inference System (ANFIS) learning to predict and analyse the interrelationship between renewable energy consumption, economic growth, and CO2 emissions of G8+5 countries. This will help the governments and industry sectors to formulate energy policies and develop energy resources sustainably. The prediction method was constructed by extracting the fuzzy rules from the real-world dataset of World Development Indicators (WDI) and generalising the relationships of the inputs and output parameters for accurate prediction of CO2 emissions. The performance of the proposed method was evaluated, and the results show its efficiency in the prediction of CO2 emissions by incorporating the import indicators, including renewable energy consumption and economic growth. The U test of Sasabuchi–Lind–Mehlum (SLM) was conducted to identify the interrelationship results obtained from the ensemble ANFIS learning and the Environmental Kuznets Curve (EKC) hypothesis. The results of SLM test found an inverse U-shape condition among all countries except Brazil. The prediction of CO2 emissions trends using the soft computing approach (ensemble ANFIS) indicated that the consumption of renewable energy reduces CO2 emissions. The proposed soft computing method was found efficient in predicting CO2 emissions. It was in line with the foreseen targets of increasing the renewable energy generation and achieving the nationally determined contributions (NDCs) objectives.

Abstract This paper presents a novel multi-period stochastic optimization model for studying long-term power generation capacity planning in developing countries. A stylized model is developed to achieve three objectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation types in the face of budget constraint, (2) to help decision makers appreciate the consequences of capacity expansion decisions on level of unserved electricity demand and its attendant impact on the national economy, and (3) to encourage the habit of periodic savings towards new generation capacity financing. The problem is modeled using a stochastic mixed-integer linear programming (MILP) technique under demand uncertainty. The effectiveness of the model, together with valuable insights derived from considering different levels of budget constraints are demonstrated using Ghana as a case study. The results indicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generation capacity to meet approximately 95% of its annual electricity demand between 2016 and 2035. Additionally, it is observed that as financial constraint becomes tighter, decisions on the mix of new generation capacities tend to be more costly compared to when sufficient funds are available.

AbstractAgriculture is one of the primary contributors to Pakistan's economy. However, over the last few decades, the atmospheric carbon dioxide emission has been amplified to a great extent in Pakistan. This amplification may cause climate change, global warming, and environmental mitigation in Pakistan. This paper empirically investigates the impact of CO2 emissions on the agricultural export trade of Pakistan over the period 1975–2017. Evidence suggests that climate change could be a primary cause for the decline in Pakistan's agriculture export trade; no studies have been carried out for this specific issue, particularly over time. In this paper, the empirical analysis suggests the negative coefficient of agriculture export trade with CO2 emissions subsequently decreases agriculture export trade, which leads to direct impact on Pakistan's economy. Both theoretical and empirical outcomes suggest that the adaptation of clean and green energies and technologies are the key to reduce pollution in Pakistan. Overall, the effects of carbon dioxide emissions on agriculture export trade are stronger in the long‐run dynamics, thus making the finding heterogeneous. Possible initiatives should be taken by the government of Pakistan to improve the agriculture sector and it should also introduce new policies to reduce the emissions of carbon dioxide.

Abstract In this paper, a system comprising a solar photovoltaic (PV)/micro‐hydropower/battery bank/converter has been designed, modelled, simulated, and optimized for the rural area of Wimana village, Rwanda. The total load has been fairly estimated for the residential electric utility needs. To satisfy the load demand, solar photovoltaic (4 kW) and micro‐hydro (15 kW) power capacity were considered as the main sources of energy to supply electricity. Either in peak load times or during low generation of primary sources, storage battery banks can also be discharged. The simulation and optimization were done based on the load demand, climatic data, the economics of integrated system components, and other parameters in which the total net present cost has to be minimized to select an economically feasible and technically capable hybrid power system. Based on the load profile of the data collected for the village, the daily energy consumption was found to be 180.99 kWh/day with a peak load demand of 18.56 kW. The net present cost and the cost of energy for the optimized power system amount to $78,763.26 and $0.0757/kWh respectively. HOMER Pro simulating software tool was used to design optimal off‐grid and energy management systems.

AbstractEnvironmental Kuznets curve (EKC) is one of the key theories of economic and environmentally sustainable development. Has the change in geopolitics in recent years affected the international collaboration in the study of the EKC? Based on the publications on the EKC included in the Web of Science, a case study of recent changes in China–US collaboration on the EKC is conducted to explore the impact of geopolitical changes on international collaborative research on sustainable development. The results show that (1) The EKC is a global study, and countries around the world have made more efforts, among which China, the United States, Turkey, Malaysia, England and other countries have made the most contributions in the global literature database and are the countries with the most development potential in this field. (2) International collaboration between China and the United States in this field has gradually increased, and scientific research collaboration between the two countries has increased under the influence of geopolitical conflicts, and collaboration has shown the characteristics of lasting stability. (3) China and the United States exhibit divergent international collaboration patterns, with Pakistan being China's foremost partner in this domain, while China serves as the US’ primary collaborator. Furthermore, China demonstrates a significantly higher volume of independently published works compared with the United States, highlighting the strengthening of China's scientific research capabilities. The number of countries collaborating internationally with China shows a different upward trend than in the United States across the three data sets set in this paper. (4) In the field of EKC hot research, China and the United States have maintained good international partnerships with other countries, and are the preferred countries for scientific research collaboration in other countries in the world. It can be seen from this that international scientific research collaboration for sustainable development represented by the EKC has shown relative stability, and geopolitical changes have not had a significant impact on international collaborative research for sustainable development.