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Effectively treating industrial SO 2 emissions depends on the synergy of different factors from the industrial SO 2 generation source to the end of treatment. Applying a whole process treatment perspective, this paper decomposes industrial SO 2 emissions into six specific driving factors in three whole process treatment dimensions (i.e. source prevention, process control, and end-of-pipe treatment), and economic scale. A temporal index decomposition analysis (Temporal-IDA), attribution analysis (AA), and spatial index decomposition analysis (Spatial-IDA) methods are then applied to quantify each dimension's treatment effect and its spatial differences. The empirical study across 30 regions in China using data from 2005 to 2015 shows that: (1) The end-of-pipe treatment is the dominant dimension for decreasing industrial SO 2 emissions, followed by process control. The contribution of source prevention to reduce industrial SO 2 emissions has begun to appear, however, there remains room for further improvement; (2) End-of-pipe treatment strength and energy intensity are key factors in reducing industrial SO 2 emissions; Inner Mongolia, Henan, and Shandong are the main contributors; (3) The treatment emphasis are different among regions; as such, there are different treatment effects across the three dimensions of the whole process treatment. Regions can be classified into four categories: the Leading type , Process-dependent type , End-dependent type, and Lagging type. Based on the empirical results, this paper identifies the policy implications of promoting whole process treatment on China's industrial SO 2 emissions. • We study China's industrial SO 2 emissions from a whole process treatment perspective. • Both temporal and spatial index decomposition analysis methods are used. • End-of-pipe treatment and process control majorly reduce industrial SO 2 emissions. • Industrial SO 2 emissions whole process treatment performances vary among regions. [ABSTRACT FROM AUTHOR]

Theoretically, agricultural insurance influences farmers' use of pesticides by changing the expected income of agricultural production. Full-cost insurance, with high guarantee and high compensation characteristics, may significantly affect farmers' pesticide use. First, this paper constructs a production function to characterize and compare the marginal incomes of insured and uninsured farmers under risk uncertainty and analyses how insured farmers can increase marginal income by increasing or reducing factor inputs. Considering scale differentiation, it discusses pesticide use strategies different types of farmers may adopt to maximize household utility. Second, using survey data of the pilot counties of full-cost insurance for wheat in Henan Province, China, the simultaneous equation model is used for empirical testing. The results reveal the following: (i) Farmers' insurance participation and pesticide application behaviour are not mutually independent. (ii) For the whole sample, full-cost insurance for wheat has a significant pesticide reduction effect. (iii) However, considering scale differentiation, pesticide application decreases significantly among insured ordinary farmers but does not change significantly among insured large-scale farmers. Third, policy measures are proposed to activate the green development function of agricultural insurance. • Construct a production function and a utility function to conduct mechanism analysis. • Estimate simultaneous equation model based on pilot area household level dataset. • Analyse the effect of full-cost insurance on pesticide input behavior. • Find full-cost insurance significantly reduces the pesticide use of ordinary farmers. • But insignificant effect on the pesticide use of large-scale farmers. [ABSTRACT FROM AUTHOR]

Starting in 2013, China's key polluting firms have been required to install continuous emissions monitoring systems (CEMS) and to publish the data for real-time oversight and public scrutiny. However, the CEMS data has rarely been used in local environmental law enforcement because its quality is still of great concern. A lack of criteria to evaluate data quality is one of the causes. In this paper, we design a comprehensive analytical framework for evaluating the quality of CEMS data, which includes completeness, accuracy, and authenticity. To demonstrate the applicability of the framework, we build a CEMS dataset for key polluting firms in Henan province from 2017 to 2019 by scraping the CEMS data from a public platform. We then conduct a comprehensive evaluation using our proposed framework. Some data quality issues are identified. About one-third of the firms did not meet official guidelines for data completeness. When comparing the CEMS data with onsite measurement results, we observe statistically significant inconsistencies in about one-fifth of the firms. In addition, we find evidence that some firms might manipulate CEMS data by strategically turning down the CEMS when a pollutant's concentration is expected to exceed the limit. Our framework can be expanded by incorporating more evaluation methods and data. We suggest that government agencies should implement a comprehensive framework to enhance the quality of CEMS data, thereby facilitating its application in law enforcement. [Display omitted] • Data quality evaluation criteria of continuous emissions monitoring systems. • Evaluating completeness, accuracy, and authenticity of the data in China. • Policy recommendations for the data quality improvement. [ABSTRACT FROM AUTHOR]

Using the social network analysis method, this paper explores the characteristics of the spatial association network structure of China's provincial transportation carbon emissions from 2005 to 2015 and its driving factors. The results are as follows: (1) The spatial association of China's provincial transportation carbon emissions exhibits an intuitive network structure. The degree of closeness which were 0.2253 and 0.2333 in 2005 and 2015 respectively shows an increasing trend on the whole. However, it hit the bottom in 2011, reaching a minimum of 0.2230, which is the "low closeness degree inflection point". (2) The spatial association network presents a significant "core-edge" distribution pattern. Specifically, in the central and eastern regions of China, Henan and Jiangsu have degrees of up to 58.621 and 44.828, which are at the network center. However, some remote regions, like Jilin and Hainan, have degrees of less than 20.000, which are marginalized. (3) The geographical adjacency, the expansion of the difference in R&D investment, and the narrowing of the difference in economic development promote the formation of the interprovincial spatial association. Therefore, in the process of controlling carbon emissions from transportation, the government should focus on the provinces at the network center and pay attention to the impact of the R&D investment on the spatial association of transportation carbon emissions. • The evolution trend of the spatial association network structure is analyzed. • The "core-edge" distribution of the spatial association network is described. • "Henan is a key governance province" in this study. • "Reducing the R&D investment difference between provinces" is proposed. [ABSTRACT FROM AUTHOR]