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As a market-based financial instrument, green credit is one of the key drivers for constructing green financial system. Green credit policy (GCP) can guide capital flow from high-energy consumption and high-pollution industries to technologically advanced emerging industrial sectors, so as to support the development of green industries and curb the emissions of polluting industries. In order to explore the extent to which the goal of green credit policy in China has been achieved, based on the micro data of China's listed enterprises from 2009 to 2017, this paper explores the impact of "Green Credit Guidelines" (2012 Guidelines) policy on the green transformation of heavy polluting industries (HPIs) by using a Difference in Differences (DID) and Propensity Score Matched-Difference in Differences (PSM-DID) models. We then analyze the mechanism between GCP and green transformation of HPIs and identify the heterogeneous effects of GCP on the green transformation of HPIs. The results show that the 2012 Guidelines significantly promote the green transformation of HPIs, through debt financing constraints and equity financing constraints. The effect of 2012 Guidelines on green transformation of small-scale and private enterprises is more pronounced. However, the positive effect of 2012 Guidelines on the green transformation of HPIs has not been sustained in the long term. Our study provides an insight for decision makers to manage green transformation of HPIs from the GCP perspective, by informing the economic consequences of GCP and pushing the industrial transformation towards green development. • Difference in differences is used to explore the impact of green credit policy on green transformation. • Debt financing constraint and equity financing constraint are main impact mechanisms. • The results are robust to a battery of robustness checks. [ABSTRACT FROM AUTHOR]

PM 2.5 emissions has become a serious issue in China with enormous regional variation in economic development, resource endowment, and consumption patterns, which leads to great variations in regional PM 2.5 emissions and embodied PM 2.5 transfer among provinces. The spatial and temporal evolution pattern of PM 2.5 and its influencing factors are important issues in the study of air pollution emissions and control, which could help policy makers allocate emission responsibilities and reduction targets. Based on the perspective of final consumption, this paper uses a multiregional input-output model to estimate PM 2.5 emissions in 30 provinces in different years in China, applies a geographically weighted regression model to analyze different driving factors, and then analyzes the spatial heterogeneity of consumption-based PM 2.5 emissions. The results show that consumption-based PM 2.5 presents a significant reduction trend, although the separation of production and consumption areas of PM 2.5 emission is further intensified. The provinces with rich energy but relatively backward economies and manufacturing industries correspond to the primary net inflow of PM 2.5 , while the provinces with strong consumption ability and the most developed economies and industries present a net outflow of PM 2.5. Moreover, the slowdown of consumption growth and the improvement of consumption quality have a positive impact on PM 2.5 emission reductions. The optimization of industrial structure has a more significant impact on pollution control and haze reduction in the western region, while the improvement of energy utilization efficiency and rational use of construction land are more conducive to the reduction of consumption-based PM 2.5 emissions in the eastern region. From the perspective of temporal and spatial changes, significant spatial differences occur between regions. Energy consumption, environmental protection investment and construction land show the trend of " high value area diffusion and low value area reduction". • Explores the spatial distribution characteristics of PM 2.5 emissions in China from the perspective of consumption. • Consider and analyze the inter-annual variation of pollutant emission transfer. • Clarifies the spatial heterogeneity characteristics of influencing factors. • Emphasizes the importance of further optimizing influencing factors to reduce haze from the consumption end. [ABSTRACT FROM AUTHOR]

Currently, China's industrial sector is a primary source of both energy consumption and environmental pollution. This paper proposes a two-stage, network-based super data envelopment analysis (DEA) approach to investigate the overall efficiency and eco-efficiency of the sub-stages in China's industrial system, including the production and three pollutant treatment stages, from 2004 to 2015. The results reveal that: (1) the industrial system's overall efficiency improved during the sample period. Specifically, efficiency has significantly improved in the production and wastewater stages, while no such improvements have been observed in the solid waste and waste gas treatment stages. (2) Both absolute and conditional β-convergences exist in the overall efficiency and sub-stage efficiencies. Over time, these types of efficiency gradually reached higher levels. (3) Although China has initially achieved positive results in improving its industrial system's overall efficiency, room for further improvement still exists. Among the industrial system's sub-stages, the waste gas treatment stage exhibits the highest efficiency, followed by the solid waste treatment stage, while the production and wastewater treatment stages have relatively low efficiency. • A two-stage network super DEA approach was proposed for industrial eco-efficiency. • The industrial system includes a production and three environmental treatment stages. • The industrial eco-efficiency of China has been improved during 2004–2015. • The performances of the industrial sub-stages are quite different. • Industrial eco-efficiency of China show distinct spatial characteristics. [ABSTRACT FROM AUTHOR]