As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

The topic of haze collaborative governance has become a hot policy issue attracting worldwide attention. However, existing studies do not pay enough attention to haze collaborative governance in the process of central environmental protection inspection (CEPI). This paper employs a tripartite evolutionary game model to explore the evolutionary mechanism of haze collaborative governance among China’s central and local governments and verifies its findings further through a case study of CEPI for air pollution in Beijing-Tianjin-Hebei and surrounding regions. The results show that haze collaborative governance is mainly affected by the performance evaluation system, policy implementation cost, clean government construction, industrial transfer trend, fiscal subsidy, environmental inspection cost, environmental accountability, and public participation. The implementation of CEPI is conducive to haze collaborative governance among central government and local governments. To promote the full implementation of air pollution control policies, the local governments need to optimize the performance evaluation system by considering economic development and environmental protection, reduce policy implementation costs by cost-sharing, and strengthen clean government construction by preventing rent-seeking corruption. In addition, the local governments need to strengthen the coordination of cross-regional industrial development and optimize fiscal subsidies to promote ecological compensation and transfer payments. For supervising the policy implementation of local governments thoroughly, the central government needs to reduce the environmental inspection cost using the evaluation framework of cost-benefit analysis, strengthen the haze governance accountability through normalizing supervision, and broaden the public’s external supervision channels. It is vital to take effective measures to carry out haze collaborative governance, providing a useful experience for developing countries.

In this article, the relationship between energy consumption and economic growth is examined from the viewpoint of China’s industrial sectors. Panel data from 37 industrial sectors in China covering the period from 1998 to 2010 was used in this study. Not only first generation panel unit root tests and panel cointegration tests, but also second generation tests that account for dependence between cross-sectional units were employed. The empirical results reveal that both energy consumption and economic growth are integrated as order one, and they are cointegrated. Panel fully modified ordinary least squares estimators show that a 1% increase in energy consumption increases the real value added of industrial sectors by 0.871%, and a 1% increase in real value added of industrial sectors increases energy consumption by 1.103%. The panel vector error correction models for causality tests are estimated by a system generalized moment method. We find a unidirectional causal relation running from economic growth to energy consumption in the shortrun. In the long run, however, there is evidence of a unidirectional causality running from energy consumption to economic growth.

As pollution in Chinese cities worsens, there is renewed interest in promoting sustainable development across the country. This article introduces the global Malmquist–Luenberger productivity index (ML) to measure green total factor productivity (TFP) growth of 270 Chinese cities over the period 2003–2013. Empirical spatial econometrics is applied to analyze the spatial spillovers and the determinants of green TFP growth. Our results show that green TFP is a useful metric for measuring development, and that spatial spillover effects do exist among neighboring cities of similar development. Finally, the study provides some policy implications.

China attaches importance to the combination of credit policy and environmental protection, tries to use credit policy tools to protect ecology and pollution prevention, and prevent environmental credit risk. With the proposal of the goal of “carbon peak and carbon neutralisation” (“double carbon”), green credit is also regarded as an important policy tool to achieve this goal. Firstly, this paper selects the time series data of green credit at the national level from 2013 to 2019 according to the official statistics and evaluates the coupling and coordinated development of credit system and environmental system based on the coupling coordination model. The results show that the two systems show well-coordinated development, but the interaction has annual fluctuations. Secondly, by calculating the provincial carbon emissions and green credit panel data from 2005 to 2019, the dynamic internal mechanism is analysed based on Panel Vector Autoregression (PVAR) model. It is found that green credit has a significant inhibitory effect on carbon emissions. The research results of this paper provide an overall evaluation of the quantity and quality of green credit for China’s banking industry. It also provides reasonable and effective support for green credit as a policy tool to promote realising the “double carbon” goal. In addition, China should maintain the consistency, stability and durability of green credit policy and continue to contribute to the low-carbon transformation of the economy and society.