Eco-efficiency is a tool for sustainability analysis that indicates how to carry out economic activities effectively. This paper assesses agricultural eco-efficiency using data envelopment analysis (DEA) and the Theil index approach. Using basic data of 31 provinces in China during 2003–2013, we analyzed the agricultural eco-efficiency development level and spatial pattern in China. The results show that the agricultural eco-efficiency of only four provinces has been relatively efficient in the entire study period, namely, Zhejiang, Hainan, Chongqing, and Tibet. The results also show that agricultural eco-efficiency was higher mainly in south of the Qinling Mountains-Huaihe River Line and north of the Yangtze River area, that agricultural eco-efficiency is mainly affected by pure technical efficiency, and that highly efficient areas are mainly concentrated in the densely populated areas, i.e., the economic developed areas (except Tibet). The Theil index results show that the agricultural eco-efficiency difference weakened between provinces in China, as did western and northeast regions, but eastern and central regions show a slight upward trend.

The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

The relationship between economic growth and environmental pollution has long been a controversial topic. However, simply the detection of the existence of environmental Kuznets curve (EKC) is not enough to understand how economic growth induced environmental pollution. This study investigated the path and mechanism of the effect of economic growth on the emission of two types of environmental pollutants, that is, industrial smoke and dust and sulfur dioxide, by using a structural equation model and a sample of 283 prefecture-level cities in China in 2005 and 2015. The research results show that economic growth exerted both direct and indirect effects on the emission of the two environmental pollutants. In addition to a direct impact through the economic scale effect, economic growth also indirectly impacted the two environmental pollutants emissions through three mediators, that is, industrial structure, technological innovations and environmental regulations. For different pollutants, the effect paths of economic growth on their emission showed both similarities and differences. First, with regards to industrial smoke and dust emissions and sulfur dioxide emissions, the effects of economic growth on the amount of these two emissions through environmental regulations and the industrial structure were negative inhibitory effects and positive promoting effects, respectively. This means that in prefectural-level cities in China, environmental regulation factors have produced some effects in reducing the emissions of these two pollutants while the industrial structure (level of industrialization) can increase the emissions of these two pollutants. However, the effect strength of these two paths shows a gradual weakening. Second, these two paths differ in effect strength and its changes. The positive promoting effects of the industrial structure on pollutant emission are significantly higher than the inhibitory effects of environmental regulation. In addition, our study also found that the direct impact path of economic growth on environmental pollution also passed significance testing, particularly in 2015. This shows that other reasons affect pollutant emission, such as system factors, spatial migration of industries and so forth.

As an important energy base in China, Shanxi Province, in its economic development, has depended heavily on coal resources. However, enormous coal consumption produces a large amount of carbon dioxide and an aggravated ecological problem. In this paper, the super efficiency slack-based measure and data envelopment analysis (SBM-DEA) model and Malmquist index were used to calculate the energy consumption and carbon emission efficiency of 11 prefecture-level cities in Shanxi in the period from 2000 to 2020. The results were as follows: (1) The primary form of energy consumption in Shanxi Province is the burning of coal, and the carbon emission efficiencies of the 11 prefecture-level cities are quite different. The technical efficiency, technological progress, and total factor productivity of Taiyuan and Shuozhou were found to be greater than 1 compared to other cities in Shanxi. (2) On the whole, although the carbon emission rate of Shanxi Province has slowed down, it still faces the problems of a large total emission base, high carbon emission intensity, and low efficiency of the unit carbon emission output. Industrial structure is the key factor restricting improvements in the efficiency of carbon emissions in Shanxi Province. (3) From the spatial and temporal evolution of the carbon emission efficiency in Shanxi Province, we can see that the carbon emission efficiency of the whole province gradually dispersed and improved from the central and northern regions to the southern regions during the period of 2000–2020. In order to achieve a low-carbon economy, Shanxi Province must optimize its industrial structure, accelerate research and development in low-carbon technologies, adjust the energy structure, and promote the construction of an energy supply system with clean low-carbon energy as the main source.

As the largest solid waste (SW) generator in the world, China is facing serious pollution issues induced by increasing quantities of SW. The sustainability assessment of SW management is very important for designing relevant policy for further improving the overall efficiency of solid waste management (SWM). By focusing on industrial solid waste (ISW) and municipal solid waste (MSW), the paper investigated the sustainability performance of SWM by applying decoupling analysis, and further identified the main drivers of SW change in China by adopting Logarithmic Mean Divisia Index (LMDI) model. The results indicate that China has made a great achievement in SWM which was specifically expressed as the increase of ISW utilized amount and harmless disposal ratio of MSW, decrease of industrial solid waste discharged (ISWD), and absolute decoupling of ISWD from economic growth as well. However, China has a long way to go to achieve the goal of sustainable management of SW. The weak decoupling, even expansive negative decoupling of ISW generation and MSW disposal suggests that China needs timely technology innovation and rational institutional arrangement to reduce SW intensity from the source and promote classification and recycling. The factors of investment efficiency and technology are the main determinants of the decrease in SW, inversely, economic growth has increased SW discharge. The effects of investment intensity showed a volatile trend over time but eventually decreased SW discharged. Moreover, the factors of population and industrial structure slightly increased SW.