As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

This paper studies the impact of carbon trading policies on breakthrough low-carbon technological innovation through a difference-in-differences model based on the panel data of 30 provinces in China. Through descriptive statistics, regression analysis, a robustness test and heterogeneity analysis of the data, the results show that: (1) carbon trading policy has a significant effect on overall low-carbon technological innovation and gradual low-carbon technological innovation, and carbon trading policy lag can significantly promote breakthrough low-carbon technological innovation. (2) The timeliness of carbon trading policy in promoting breakthrough low-carbon technological innovation is different in different regions. The effect can quickly be displayed in some provinces, but there is a lag effect in other provinces. (3) Lags in carbon trading policy have promoted a significant increase in renewable energy technology patents and CCS-related technology patents, and contributed more to the increase in renewable energy technology patents.

Exploring the co-benefits of low-carbon tech-innovation in response to climate change on haze pollution is an important foundation for China’s ecological construction, and also a key path to the common goal of carbon and haze reduction. Based on the STIRPAT model and EKC hypothesis, the dynamic spatial Durbin model (SDM) is constructed to empirically analyze the co-benefits and the mechanism of low-carbon tech-innovation on haze pollution in 30 Chinese provinces from 2006 to 2018. The results show that 1) haze pollution in different regions of China shows significant temporal and spatial correlation. 2) China’s low-carbon tech-innovation brings the co-benefits of haze pollution suppression and long-term positive externalities between regions. 3) Environmental policy and industrial structure play a moderating and mediating role, respectively, the former produces the “innovation offset” effect. 4) Both types of low-carbon tech-innovation can suppress haze pollution, but gray tech-innovation shows better haze control ability and cross-regional diffusion ability. Therefore, a long-term mechanism for haze control and joint prevention and control should be established to prevent the rebound and agglomeration of haze, and balance the development of different types of low-carbon technologies to achieve coordinated control of carbon emissions and haze.

The operational efficiency of insurance companies is crucial for their long-term stability and sustainable development. Climate risk has emerged as a significant factor affecting insurers’ operational performance in the context of global climate change and sustainable development goals. Although prior research provides a solid foundation, further exploration is needed to clarify how climate risk influences insurers’ efficiency and underlying mechanisms. This paper uses panel data from 248 Chinese insurance companies spanning 2011 to 2021 to construct a climate risk indicator and systematically examines the potential pathways through which this indicator influences operational efficiency. Precisely, absolute temperature deviation measures physical climate risk, and an entropy-weighted method captures climate transition risk; the DEA model evaluates operating efficiency. A fixed-effects model reveals that physical climate risk may adversely affect operational efficiency, while climate transition risk demonstrates a U-shaped relationship with efficiency. Mechanism analysis shows that physical climate risk increases exposure to natural disaster losses, whereas transition risk may encourage green insurance development. Heterogeneity emerges across insurer types and between coastal and non-coastal regions, with resilient infrastructure mitigating the adverse effects of physical risks and insurance technology driving gradual transformation to offset initial transition risks. Overall, this study expands the perspective on how climate risk shapes the insurance industry’s sustainable development, offering theoretical and practical insights for policymakers to optimize risk management and promote green finance.

Research on the EU’s Carbon Border Adjustment Mechanism (CBAM) has predominantly examined its implications for climate governance and export trade yet overlooked how enterprises adapt their foreign investment strategies. Using panel data from Chinese listed companies between 2011 and 2022, this study employs the CBAM as a quasi-natural experiment and applies a difference-in-differences (DID) model for analysis. Our findings indicate that the CBAM has a significant positive impact on outward greenfield investments, as robustly validated through a series of rigorous robustness checks. Mechanism analysis reveals two operational channels: trade restructuring effect (reduced export shares) and innovation-driven demand effect (enhanced R&D intensity). Heterogeneity tests further indicate more substantial CBAM responsiveness among eastern coastal firms, non-state-owned enterprises, and those pursuing horizontal production-oriented expansions. This study contributes to the literature on CBAM’s effects and offers practical recommendations for enterprises to mitigate CBAM’s impact via greenfield investments.

Addressing energy poverty is integral to the United Nations Millennium Development Goals, and clean energy technology serves as an essential tool in mitigating this issue. Existing studies pay less attention to the correlation between the two. In this study, we quantify the spread of clean energy technology using patent citation information and analyze balanced panel data from 30 provinces in China spanning the years 2004 to 2019. The findings reveal that the diffusion of clean energy technology not only directly reduces energy poverty but also indirectly alleviates it by enhancing energy efficiency and fostering employment. However, the positive impact of technology on poverty is affected by human capital. Specifically, when the number of college students per 10,000 people in a province surpasses 179, technology diffusion becomes effective in alleviating energy poverty. Furthermore, the efficacy of this mitigation varies significantly based on different regions. Technologies originating from developed economies such as the United States, Japan, and the European Union exhibit a more substantial impact than domestic alternatives. Additionally, the effect of alleviating energy poverty is more significant in the eastern region. Therefore, we make policy recommendations for alleviating energy poverty through the use of incentive policies, exploring differentiated models of clean energy technology development, and strengthening international cooperation.