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[Display omitted] • Exploring the industrial water-land-energy-carbon interaction help promote resource conservation and low-carbon development. • Constructing evaluation indicators and decomposition model to assess the carbon emission effect of industrial water and land use. • Industrial carbon emission intensity of water and land use in China increases, and inter-provincial difference expands. • Industrial matching index of water and land resources in China presenting great spatial agglomeration. • Matching index of water and land resources is the main inhibitory factor for all provinces in China. The industrial production requires the input of water, land and energy, and the water-land-energy interaction generates carbon emissions. Investigating the industrial water-land-energy-carbon nexus and exploring the influencing factors of the carbon emissions help promote the intensive use of water resources, land resources, and energy, as well as the low-carbon development of industry. This paper applied kernel density estimation and spatial auto-correlation analysis to explore the spatio-temporal variation of industrial carbon emission effect of water and land use and the industrial matching status of water and land resources in China, and then analyzes the influencing factors to industrial carbon emissions by introducing water and land resource factor into Kaya identity and Logarithmic Mean Divisia Index (LMDI) model. The main conclusions are: (1) From 2006 to 2020, China's industrial carbon emissions increased by 82.02%. The industrial carbon emission intensity of water use (CEIWU) and the industrial carbon emission intensity of land use (CEILU) increased by 158.02% and 55.53% respectively. The inter-provincial difference of CEIWU and CEILU expanded considerably, and the number of provinces with high CEILU had a large increase. (2) The industrial matching index of water and land resources (MIWL) in China showed a fluctuating downward trend, presenting great spatial clustering, and the provincial MIWL were mainly concentrated in the H-H and L-L regions. (3) The contributing and inhibitory effect of each influencing factor varied on regional and provincial level. The economic output of water use is a contributing factor to all regions and provinces, while the MIWL is an inhibitory factor to all regions and provinces. [ABSTRACT FROM AUTHOR]

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]

• The potential gains and carbon emissions reduction of carbon trading are detected. • Three scenarios of carbon trading schemes are considered and compared. • Sectoral trading may create potential gains of 268.02 trillion yuan in 2006–2015. • Carbon emissions may decrease by 17.17 billion tonnes under sectoral trading scheme. • Sectoral trading decreases industrial carbon intensity by 19.80% during 2015–2020. Carbon trading is an important market tool in driving growth and carbon dioxide emissions reduction in industrial sectors in China. This paper attempts to assess the impacts of carbon trading on economic output and carbon dioxide emissions reduction in China's industrial sectors by employing the data envelopment analysis (DEA) based optimization models, based on three carbon trading schemes, i.e., no trading (NT), sectoral trading (ST), and sectoral-and-temporal trading (STT) during 2006–2015. Comparing with the no-trading scheme, the results indicate that, (1) the ST and STT schemes may create potential gains of 268.02 and 612.26 trillion yuan in the whole industrial during the study period, i.e., the industrial value-added would be increased by 55.17% and 73.76%, respectively; (2) the ST and STT schemes could reduce 17.17 and 19.22 billion tonnes of carbon dioxide emissions, respectively, accounting for 58.30% and 65.25% of emissions reduction in the whole industry; and (3) if carbon trading were adopted in China since 2006, its carbon intensity of industrial sectors would decrease by 34.89%, 47.44% and 19.80% under the ST scheme, and 58.93%, 31.50% and 10.25% under the STT scheme during the11th, 12th and 13th Five-Year Plan periods, respectively. [ABSTRACT FROM AUTHOR]