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The study investigates the nexus of CO2 emissions, tourism, fossil fuels, and GDP growth using China’s data from 1970 to 2019. The research applied the upset U-molded EKC and the ARDL -models to calculate the time series stationarity variables. The results showed that in the initial enlargement phases, a sophisticated GDP adversely impacts CO2 emissions, then a higher GDP positively influences CO2 emissions. The development of tourism, use of fossil fuels (coal and oil), and population growth show an important influence on CO2 emissions but the use of gas and electricity has little effect on CO2 emissions. In contrast, foreign direct investment besides population development had little effect on increasing CO2 emissions. Retreating foreign direct investment, strengthening the use of sustainable electricity, and improving transportation for explorers, especially the green tourism business, are excellent ways to reduce environmental degradation in China.

The pollution caused by foreign investment has become a serious concern of the host government and academia. Many studies have discussed much about the environmental impact of China’s OFDI in countries along the Belt and Road Initiative (BRI) region, but ignored the impact of the BRI itself on China’s OFDI. This study uses the Global Malmquist–Luenberger index to measure the green technology spillover of China’s OFDI from 2005 to 2018 and studies the impact of BRI on this spillover effect with the DID (difference-in-difference) method. The results show that (1) the BRI has significantly increased the green technology spillover of China’s OFDI in countries along the routes. The heterogeneity study shows that such an effect is significant in middle- or low-income countries with high institutional quality or poor environmental performance, but not obvious in other countries. (2) The BRI promotes green technology spillover through the mechanism of increasing R&D investment, improving the environmental system, and accelerating the flow of production factors. This study provides a useful reference for developing a greener OFDI and promoting the sustainable development of regional cooperation.

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.

The collection of publicly available databases about climate change and its impacts on natural and human systems is unprecedented and ever-growing. However, the requirements of information can vary widely among users depending on their region, socioenvironmental context, and interests. Moreover, in the current era of active mitigation and adaptation policies, information needs are frequently not satisfied even by these massive and variated collections of databases. The development and use of emulators can help closing this information gap by allowing users to approximate the output from complex models and create user-defined experiments, without being technically or computational demanding on the user. Here, a simple emulator of the EPIC biophysical crop model is presented which is able to adequately reproduce the changes in rainfed maize and to create projections for user-defined scenarios. Moreover, it allows to produce risk measures that are not available with the original model. The proposed methodology is illustrated with a case study of rainfed maize production in Mexico for a reference emissions scenario (SSP370) and two user-defined international mitigation policy scenarios. These scenarios represent 1) current international mitigation commitments and 2) a scenario in which China withdraws from international mitigation efforts. Results showed that, under the reference scenario, climate change could have widespread consequences on rainfed production all over the country with decreases in yields reaching up to 80% in the southeast and northeast of the country. These impacts can be partially modulated by the moderately ambitious mitigation commitments assumed in recent international agreements if all countries comply. However, a potential withdraw of China from these efforts would significantly reduce any benefits from international mitigation. Under all scenarios, changes in productivity impose increasing risks for already vulnerable populations and considerable economic costs at the state and national levels. These results suggest the urgent need for critical planning for adaptation in the agricultural sector of the country.

The research conducted in this paper aims to examine the role of energy innovations, digital technological transformation, and environmental performance in enhancing the sustainable economic development of the European Union (EU) countries, widely shaped by the globalization process. An advanced empirical analysis is configured on a cross-sectional dataset of EU-27 Member States compiled at the level of 2018 based on several modern econometric procedures, namely robust regression, structural equation modelling (SEM) and network analysis through Gaussian graphical models (GGM). We apply the econometric procedures to firstly identify and assess the direct, indirect, and total interlinkages between all considered variables, as well as their further cumulated spillover impact on sustainable economic development. EU countries are afterward clustered according to the Ward method inset on hierarchical clustering for an in-depth assessment and tailored policy design by accounting for the level of financial and trade globalization (captured through the KOF Index of Globalization), environmental performance and sustainability (captured through the Environmental Performance Index—EPI), and the degree of integration of digital technologies (proxied through the Digital Economy and Society Index—DESI). Main results highlight that there are significant beneficial effects induced by energy innovations, increased environmental performance and digital transformation on the sustainable development of EU countries, with notable differentiation among them. Policy guidelines and strategic directions are also enhanced and largely presented within the paper.

Wind, hydropower, solar, and other renewable energy (RE) replace conventional energy sources when the global green and low-carbon transition is broadly considered. Therefore, the current study investigates the core determinants of renewable energy development (RED) in China from 1990 to 2020: carbon emissions efficiency (CEE), green finance (GF), urbanization (URB), openness (Op), and gross domestic product (GDP). However, advanced econometric techniques are used to investigate the desired objectives, such as an interactive fixed effects panel test, threshold panel regression, Pooled Mean Group, and Bootstrap Panel causality test. The research findings shows a negative association of CEE with renewable energy development. According to these findings, which are in line with the optimization method, it seems that RED is doing an outstanding job of reaching carbon neutrality. However, there found an increase in RED via green finance and economic growth over time. Also, a negative relationship exists between urbanization and openness with energy development. In comparison, zero-emission competition is expected to reshape the globe’s demographics.

This study focuses on determining the relationship between carbon emissions, financial development, population, green technology innovation, energy Consumption, and employment rate from 1980 to 2019 in China. The study applies the unit root test, bootstrapped ARDL cointegration, and the Granger causality to examine the data properties and association between the variables of interest. Empirical findings indicate that green technology innovations and financial development play a major role in environmental protection, specifically in the long run. In contrast, energy consumption and employment rate are more vulnerable to protecting the natural environment in China. On the other side, the findings under short-run estimation do not support the role of green technology innovation in reducing environmental degradation. Based on the empirical findings, it is suggested that a strong financial system would help to achieve long-run sustainability and the emissions mitigating effects can be further strengthen by implementing green technologies across industries. In doing so, strict environmental regulations can regulate the financial and traditional industrial sector in adoption of energy efficient technologies.

The impact of economic growth and energy use is still controversial regarding sustainability, and researchers have limited consensus in this regard. Electricity is considered more environmentally friendly compared with direct fossil fuel consumption. However, many developed economies still depend on fossil fuel sources for electricity generation. Therefore, this study attempted to verify the relationship between electricity consumption and carbon emissions in developed economies in the Group of Twenty (G20). Economic growth and foreign direct investment are other important variables for analyzing this relationship. For this purpose, a dataset from 1995–2018 was generated. The study used econometric methods including cross-sectional dependence, cointegration, Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS) estimators, and the Pair-wise panel Granger causality test to examine the relationship between dependent and independent variables. The findings show a positive relationship between electricity consumption and CO2 emissions. This indicates that electricity production is still dependent on sources that help increase CO2 emissions in G20 countries. Furthermore, the results show that gross domestic product and its square term confirm the Environmental Kuznets Curve (EKC) theory for these economies. These results suggest that policymakers promote green and clean electricity sources for sustainable economic growth.