• Energy Research

This study empirically investigates the impact (overall, regional, and seasonal) of weather and climate extremes on basic human needs by employing a new poverty index, i.e., the Human Needs Index (HNI), in the United States of America. Detecting the contemporaneous correlations between errors, we apply second-generation unit root tests on monthly statewide panel data ranging from January 2004 to December 2018. The results obtained through cross-sectional time-series feasible generalized least square (i.e., FGLS) regression suggest that human necessities statistically and significantly correlate with a positive response to the weather extremes (cold, low precipitation) and with extreme events (drought, flood). However, the response is the opposite of that in the case of high precipitation. The seasonal variations in necessities indicate that there is a significant escalation of the needs between July and December (January is taken as the reference month), but, in February, they substantially shrink. Furthermore, the regional implications imply that, with the West of the US taken as the reference region, needs are significantly augmented in the Midwest; conversely, in the east and the south, they are significantly decreased. We also observe that some interaction effects, such as high precipitation and personal income as an interaction term, significantly, but negatively, correlate with HNI, indicating a 0.025% shared effect. Contrary to these findings, high precipitation, coupled with supplements to wages and salaries, shows a positive joint association of 0.274% with HNI. Besides, low precipitation, coupled with the unemployment rate, personal income, and flooding, shows an additional positive and significant mutual effect, while low precipitation has a negative effect on basic human needs when coupled with supplements to wages and salaries. The corresponding estimated interacting coefficients are 3.77, scoring 0.053%, 0.592%, and −0.67%, respectively.

AbstractThis paper aims to estimate the spatial dynamic evolution of renewable energy development efficiency and justify the dimensions that impact renewable energy development efficiency. The study applies the following methods: the ultraefficient slack‐based model (SBM) (to measure the efficiency of renewable energy development); the Dagum‐Gini coefficient decomposition process (to measure the interregional differences in the development of renewable energy efficiency); nuclear density estimation (to measure the dynamic distribution); the Markov model (to forecast renewable energy development efficiency); and the Tobit model (to justify the influencing factors of renewable energy development efficiency). The empirical findings confirm that the overall regional gaps in renewable energy development efficiency in China are widening year by year. The average value of renewable energy development efficiency increased from 0.932 in 2006 to 1.078 in 2020. The mean Gini coefficient increased gradually from 0.028 in 2006 to 0.174, with mean differences exceeding the average growth trend after 2011 and slowly decreasing post‐2016. There is polarization in the eastern region, while there is no polarization in the northeast. The overall level of renewable energy development efficiency in the middle and western areas is improving and showing a trend of absolute difference narrowing. In addition, economic development, green finance, technological progress, urbanization rate, and economic openness are conducive to renewable energy development efficiency, and renewable energy development efficiency is in a rapid development trend. Considering the findings, China should implement targeted regional development strategies, enhance green finance mechanisms, promote technological innovation, and align urbanization policies with renewable energy goals to reduce regional disparities and accelerate sustainable renewable energy development.

For decades, environmental degradation has become a universal challenge, and for sustainable environment quality, a true and broader proxy is vital. Pakistan is an ecological deficient country in the world, being the sixth-largest economy (population-wise). This study investigates the prime sources of environmental degradation through ecological footprint in Pakistan. The yearly time-series data spanning 1972 to 2020 is utilized for a set of regressors as fossil fuel energy consumption, trade openness, arable land, industrial share to GDP, economic growth, and population growth. We use various econometric techniques, the bounds test, ARDL (short and long run) model, FMOLS, and Granger causality test. Bounds test confirms the existence of cointegration among variables included in our model. The ARDL estimates suggest that fossil fuel energy consumption, trade openness, and population growth are the leading factors affecting the environment. Fossil fuel consumption and population growth significantly damage the environment in the short and long run. Contrasting to that, trade openness is substantial to the environment quality. The FMOLS approves the robustness of the cointegrating findings. Moreover, a unidirectional causal relationship from economic growth to the ecological footprint (GDP → EFP). And also, the ecological footprint of arable land (EFP → AL) is witnessed. At the same time, bidirectional causality is found between growth rate and fossil energy consumption (GDP ↔ FEC). Lastly, we recommend some policy options to improve environmental quality in Pakistan.

A green economy refers to a modern form of harmony between the environment and the economy. China showing the fastest economic growth in the world has entered into a new phase of advance, facing a critical industrial transformation and progression. The paper aims to analyse China's green economic development considering the differences in development of regions. The study applied the ultra-efficient slacks-based measure model to scrutinize China's green economic development efficiency. Dagum Gini coefficient and Kernel density methods are used to estimate spatial characteristics, local adjustments, and dynamic evolution trends. The analysis is based on an annual dataset of 30 Chinese provinces from 2010 to 2019. The findings did not confirm extensive China's green economic development. In contrast, the development efficacy reveals an influential drive over the years. Regional green development is detected as unstable and diverges due to interregional differences. The findings showed that environmental regulation, government investment, industrial structure, education development were 0.0648, 0.00154, 0.0035 and 0.118 (significant at 5% and 1%), respectively. Besides, they stimulate the green economic development in the analysed regions. However, urbanization and openness of economy had the negative value. It confirmed their restriction impact on the green economic development. In addition, the findings showed that ongoing Chines policy on management of environmental development is the priority direction and provoke the declining the environmental pollution. Besides, the modernization and optimization of the Chinese industry structure stimulate the further green economic progress.