• Energy Research
• 2021-2025close
• TR close
• Frontiers in Environmental Science close

Previous literature documented the importance of natural disasters and their impact on economic performance, but it ignored the effects on the environment. This study examines the effect of natural disasters on the economic and environmental performance of BRICS economies over the period 1995–2019. This study applies panel autoregressive distributed lag (ARDL) and panel quantile regression approaches. The empirical findings show that natural disasters decrease economic growth but increase CO2 emissions. The findings of panel quantile regression display a significant negative impact of natural disasters on economic growth from the middle (30th) to higher (80th) quantiles. However, natural disasters significantly increase carbon emissions from the middle (50th) to higher (95th) quantiles. This study suggests the importance of proper planning for the management of natural disasters.

Facing the increasingly severe climate situation, China strives to improve its Nationally Determined Contributions, promising to reach its carbon peak by 2030. Accurately predicting the future demand quantity and changing the trends of coal resources is the key to maintaining national energy security and achieving the goal of “carbon peak” and is also an important research topic in the future. To improve the prediction accuracy, this study sorts out eight common factors affecting the coal demand from the aspects of the economy, population, and energy. The grey relational analysis method was used to describe the degree of importance of each factor and screen out the relatively vital factors. The system dynamics model for coal demand in Shanxi Province was also established. The coal demand and carbon emission trends in Shanxi Province from 2021 to 2030 were predicted under business-as-usual, low-speed and high-speed development scenarios. The study results show that: 1) During the 14th Five-Year Plan to 15th Five-year Plan, the trend of coal demand has changed from increasing to decreasing. Under the three scenarios, the coal demand in 2021 is expected to be 372.03 million tons, 365.97 million tons, 360.04 million tons, and it is expected that the coal demand will peak in 2025, and will reach 394.77 million tons, 390.27 million tons, and 385.66 million tons under the three scenarios, respectively. 2) With the development of population and economy, the total energy demand shows a continuously increasing trend. It is estimated that the total energy consumption will reach 237.46-242.93 million tons of standard coal in 2030, and the total energy demand will continue to grow in the foreseeable future. 3) The carbon dioxide emission is closely related to the coal demand, showing a similar variation trend. Under the business-as-usual development scenario, carbon emissions in 2021 are 699.62 million tons, and carbon emissions are expected to reach 681.12 million tons in 2030. The low-speed development and high-speed development scenarios have different degrees of energy saving and emission reduction benefits. Compared with the business-as-usual development scenario in 2030, the carbon dioxide emissions will be reduced by 1.64 million tons and 3.56 million tons respectively. Finally, we put forward important policy measures to promote the economic transformation and upgrading of Shanxi Province and accelerate the realization of the “carbon peak” goal.

This paper adds to the existing body of knowledge by incorporating the role of fiscal decentralization (FD) in influencing CO2 emissions. Therefore, this study looked at the effect of FD on CO2 emissions in the presence of nonrenewable energy consumption (NRE), renewable energy consumption (REN), gross domestic product (GDP), and trade openness (TOP) for the period 1994–2018 in Japan. Thus, the current work intends to fill this knowledge gap by employing econometric techniques such as Bayer and Hanck cointegration, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR). Additionally, the frequency domain causality analysis is used in the investigation to determine the causal impact of FD, NRE, REN, GDP, and TOP on CO2 emissions. The novelty of the frequency-domain approach is that it can differentiate between nonlinearity and causality levels and show causality among parameters with different frequencies. The DOLS, FMOLS, and CCR results reveal that NRE, GDP, and TOP augment CO2 emissions in Japan, whereas FD and REN increase the quality of the atmosphere. Furthermore, the frequency causality test results show that FD, REN, GDP, and TOP have implications for CO2 emissions in the long run, while NRE raises CO2 emissions in the medium run. As a policy direction, the current study suggests expanding renewable energy consumption in Japan by emphasizing more on Sustainable Development Goals (7, 8, and 13).

Flooding is the main disaster type in Taiwan and is usually caused by typhoons and heavy rainfall. To understand the flood impacts in Taiwan caused by increasing rainfall due to global warming, this study adopts a high-resolution atmospheric model (HiRAM) under the representative concentration pathway (RCP) 8.5 scenario to project future changes in flood impact. For the flood simulation, the SOBEK flood model was used to determine the maximum accumulated flooding depth and flood probability in the two periods of the present and the middle of the 21st century. Yilan County, one of the most flood-prone areas in Taiwan, was chosen as a demonstration case for the development of flood impact maps. According to the results of flood map application, flooded areas were predicted to increase in the middle period of the 21st century due to increasing rainfall, especially in paddy fields, maricultural farms, and stock farms. From the base period to the middle of the 21st century, the area of flooding impacts was projected to increase from 24% to 40% in paddy fields, from 9% to 15% in maricultural farms, and from less than 1% to 9% in stock farms. These results show that the development of flood maps can help elucidate the actual impacts of climate change in Taiwan and serve as a scientific basis for adaptation actions.

The purpose of this study is to determine the impacts of foreign direct investment (FDI), renewable energy (RE), energy consumption index (ECI), Globalization (GLO), and green technology innovation (GTI) on environmental pollution using a time series data from 1980 to 2019, using DARDL assessor to look at how markers with high levels of petroleum derivatives distorted the explanatory variable in China. The results showed that GTI contaminated environmental sustainability (ES). The polluted safe house notion claimed that FDI has a negative impact on the country’s inherent character. Finally, people are waking up to the importance of ES. Single-headed causalities from GTI to carbon emissions were detected in the middle of the other causes and fossil fuel byproducts need to change into green energy resources to reduce environmental pollution. Finally, the research proved that FDI is a major source of environmental pollution in China. According to the findings of the DARDL research, foreign direct investment and urbanization, green technological innovations, and China’s environmental pollution policy direction are all congruent with each other.

As digital finance ushers into a new era, carbon emissions in China have been peaking, highlighting the necessity of carbon neutrality. This work uses a dynamic spatial Durbin model, combined with a mediating effect model of the data from 30 provinces from 2011 to 2019, to explore the impact, transmission paths, and spatio-temporal heterogeneity of digital finance (Df) on carbon emission intensity (Cg). Meanwhile, the validation explores the mediating role of technological innovation (Rd), industrial restructuring (Is), and entrepreneurial effects (Es) in the process of digital finance influencing green low-carbon development. The empirical results show that: first, digital finance (Df) has a promoting effect on regional CO2reduction capacity (Cg), and this conclusion still holds under multiple robustness tests; second, digital finance (Df) can promote the regional CO2reduction capacity (Cg) through two paths, namely, promoting technological progress (Rd) and optimizing industrial structure (Is); third, the impact of inclusive digital finance on CO2emission intensity is heterogeneous. By analyzing regions with different economic development levels, we found that digital inclusive finance in the eastern region can enhance CO2reduction capacity, while in the central and western regions, the impact is not significant. Given this situation, China, to achieve carbon neutrality, should boost financial development’s ability to reduce carbon emission, promote technological progress, and optimize the industrial structure, thus forming a green and low-carbon economic cycle. This paper fills the research gap on how digital finance can effectively promote green development while exerting economic effects, and at the same time, enriches the literature on factors influencing green and low-carbon development.

This study adopts a relatively infrequent approach to explore the impact of environmental regulation and technological innovation on energy efficiency (EFF) based on the undesired superefficiency SBM and random Tobit model. To study the universal EFF, we establish the undesired superefficiency SBM model, which is composed of three input indexes such as energy, one expected output index, and three pollution emissions as the undesired output index, consider the EFF of the sample with an effective decision-making unit (DMU) value less than 1, and calculate with the MATLAB software, according to panel data of 30 Chinese provincial-level regions from 2001 to 2019. The empirical results show that the EFF has an N-type trend in the eastern, central, and western regions of China. When exploring the impact of environmental regulation and technological innovation on EFF, we conduct an empirical analysis of the eastern, central, and western regions with the random Tobit model. The regression results indicate that the impact of environmental regulation on EFF in the different regions varies significantly. Technological innovation has different impacts on EFF in different regions and is the main influencing factor of EFF. Moreover, we strive to analyze the impact of cross-term environmental regulation and technological innovation and find that the cross-term has a significant positive impact on EFF in each region. These results emphasize that environmental regulation and technological innovation have positive or negative impacts on EFF, and both impacts may exist simultaneously, which is a perfection of the EFF theory.