To cope with huge carbon emission pressure, China has implemented a carbon emissions trading pilot policy that aims to provide reasonable suggestions for the smooth operation of the national carbon market. This paper selects the provincial panel data in China from 2005 to 2019 and uses the propensity score matching-difference in difference (PSM-DID) method to evaluate the carbon emission policy’s reduction effect. Based on carbon emissions (CE) and carbon emission intensity (CI), provinces and cities are divided into four regions, and each region is verified by spatial difference analysis. Furthermore, the mediating effects of carbon emission reduction through the dual aspects of technological progress and industry structure are also discussed. Results verified that, (1) under the carbon emission trading policy, regional carbon emissions and carbon emission intensity are both significantly reduced. (2) Technological progress helps to reduce carbon emissions, while industrial structure shows no obvious contribution. (3) The four regions all show ideal emission reduction effects, of which the High CE-High CI region shows the best, but is greatly restricted by techniques. The industrial structure of the High CE-Low CI region needs to be further optimized for carbon reduction. In the Low CE-High CI region, the carbon emissions brought by economic development fail to effectively improve per capita GDP. The Low CE-Low CI region contributes greatly to carbon emission reduction with technical advantages.

The enormous discharge of industrial waste seriously hinders the sustainable development of cities. However, most studies only involve a single or limited category of industrial pollutants, ignoring the environmental pressure caused by multiple resources and environmental factors. This paper combines input-output analysis and ecological network analysis to construct an industrial waste metabolic input-output (IWMIO) model, which explores the industrial waste discharge and discharge relationships among different sectors in Jiangsu Province from the three aspects of industrial wastewater, industrial waste gas, and industrial solid waste. The results show that the indirect discharge of industrial waste is greater than the direct discharge in the industrial waste metabolism system. TI (Tertiary industry), CI (Chemical industry), SPM (Smelting and pressing of metals), and PSEH (Production and supply of electricity and heat) dominate the industrial waste metabolism system. In addition, MWC (Mining and washing of coal), MNMP (Manufacture of non-metallic mineral products), SPM (Smelting and pressing of metals) have more mutualism and competition relationships with other sectors, so the control of industrial waste discharge in these sectors contributes to achieving emission reduction targets. Based on the research results, this paper proposes corresponding policy recommendations such as considering both direct and indirect emissions of sectors when formulating waste reduction policies and developing pertinent industrial waste reduction programs based on the characteristics of the identified sectors. The results of this paper are helpful to identify the dependence and influence relationships of various sectors in the industrial waste metabolism system, promote industrial waste discharge control, and provide theoretical support for the adjustment of industrial structure and the formulation of related policies in Jiangsu Province.

Sustainability has recently been acknowledged as a crucial issue in infrastructure projects. Developing a model to evaluate project sustainability according to sustainability indicators plays a major role in promoting the sustainable development of water environment treatment public‐private partnership (PPP) projects. Traditional sustainability assessments are mostly based on the triple bottom line (economic, social, and environmental) and lack a more integrated indicator system. To connect the research gap, this paper identifies 27 factors that affect the sustainability of water environment treatment PPP projects from five dimensions: economy, society, resources and environment, engineering, and project management using exploratory factor analysis. The fitting degree between the model and original data is verified by confirmatory factor analysis. The results showed that the fitting was successful. This paper makes two contributions: first, it provides a comprehensive sustainability evaluation indicator system from five aspects, laying a foundation for the evaluation of project sustainability. Second, this study defines a methodology to evaluate and rank factors, identifies the indicators that show the most significant impact on project sustainability in the five dimensions, which provide a reliable reference for the public and private sector to take appropriate measures to improve the sustainability level of water environment treatment public‐private partnership projects.

Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework.

Regional trade leads to carbon transfer, which in turn raises the issue of carbon inequality. However, less existing research has focused on carbon inequality within megaregions. Combining multi-regional input-output analysis, carbon Gini coefficients and carbon deviation coefficients, this paper presents a comprehensive analysis of carbon equity in inter-provincial trade in the Yangtze River Economic Belt (YEB) from the perspective of economic benefits and environmental pressure. The results show that: (1) Carbon emissions from the production and consumption sides of the 11 provinces in the YEB vary considerably. (2) Significant carbon inequality exists in the YEB region. This is manifested in the unequal relationship between the transfer of embodied carbon emissions and economic benefits, as well as the difference in carbon deviation coefficients. Based on the results of our research, this paper can help provide theoretical support and decision-making reference for implementing a differentiated carbon emission reduction responsibility mechanism and establishing a coordinated carbon emission reduction responsibility system.

Virtual water flows have a profound impact on the natural water system of a country or region, and they may help conserve local water resources or exacerbate water scarcity in some areas. However, current research has only focused on the measurement of virtual water flows, without analysis of the causes of virtual water flow patterns. This study first obtained virtual water flow patterns across provinces by constructing a multi-regional input–-output (MRIO) model of the Yellow River basin in 2012 and 2017, and then analyzed its driving factors by applying the extended STIRPAT model to provide directions for using virtual water trade to alleviate water shortages in water-scarce areas of the basin. We found the following: (1) The Yellow River basin as a whole had a net virtual water inflow in 2012 and 2017, and the net inflow has increased from 2.14 billion m³ to 33.67 billion m³. (2) Different provinces or regions assume different roles in the virtual water trade within the basin. (3) There is an obvious regional heterogeneity in the virtual water flows in different subsectors. (4) Per capita GDP, tertiary industry contribution rate, consumer price index, and water scarcity are the main positive drivers of virtual water inflow in the Yellow River Basin provinces, while primary industry contribution rate, per capita water resources, and water use per unit arable area promote virtual water outflow. The results of this paper present useful information for understanding the driving factors of virtual water flow, which could promote the optimal allocation of water resources in the Yellow River basin and achieve ecological protection and high-quality development in this area.