Large amounts of wastewater discharge have emerged as a burden in the process of industrialization and urbanization. In this study, a dynamic wastewater-induced input-output model is developed to systematically analyze the related situation. The developed model is applied to Guangdong Province, China to analyze its prominent characteristics from 2002 to 2015. Combining input-output analysis, ecological network analysis and structural decomposition analysis, the developed model reveals issues of direct and indirect discharges, relationships among various discharges, and driving forces of wastewater discharges. It is uncovered that Primary Manufacturing and Advanced Manufacturing dominate the system because of significant temporal and spatial variations in wastewater discharge. In addition, Manufacturing of paper, computer and machinery and Services are the key industries responsible for large amounts of wastewater discharge and unhealthy source-discharge relationships. The largest wastewater discharge occurred in 2005 and indirect wastewater discharge is the main form. Furthermore, final demand is found to be the biggest driving force of wastewater discharge. Finally, a three-phase policy implementation system implemented in stages proposes solutions to wastewater problems.

Whilst attention is increasingly being focused on embodied pollutant emissions along supply chains in China, relatively little attention has been paid to dynamic changes in this process. This study utilized environmental extended input-output analysis (EEIOA) and structural path analysis (SPA) to investigate the dynamic variation of the SO2 emissions embodied in 28 economic sectors in Chinese supply chains during 2002–2012. The main conclusions are summarized as follows: (1) The dominant SO2 emission sectors differed under production and consumption perspectives. Electricity and heat production dominated SO2 emissions from the point of view of production, while construction contributed most from the consumption perspective. (2) The embodied SO2 emissions tended to change from the path (staring from consumption side to production side): “Services→Services→Power” in 2002 to the path: “Construction and Manufacturing→Metal and Nonmetal→Power” in 2012. (3) Metal-driven emissions raised dramatically from 15% in 2002 to 22% in 2012, due to increasing demand for metal products in construction and manufacturing activities. (4) Power generation was found to result in the greatest volume of production-based emissions, a burden it tended to transfer to upstream sectors in 2012. Controlling construction activities and cutting down end-of-pipe discharges in the process of power generation represent the most radical interventions in reducing Chinese SO2 emissions. This study shed light on changes in SO2 emissions in the supply chain, providing a range of policy implications from both production and consumption perspectives.

As one of the countries with the most severe water resource problems, China faces enormous challenges in the intensive use of water resources. Rapid economic development has led to serious waste of water resources in the industry, resulting in path dependence on water-consuming technologies, namely the concept of ‘water lock-in’. This study aims to estimate the water lock-in effects in various industries in China from 1997 to 2017. To this end, a novel combination of the input–output analysis and social network analysis methods is used to calculate ‘integrated, intra-sectorial and inter-sectorial’ water lock-in, identify the complex water resource dependence relationship and explore the dynamic evolution process of the lock-in mechanism. The research results are as follows. (1) From 1997 to 2017, the integrated, intra-sectorial and inter-sectorial water lock-in coefficients decreased by 82.08%, 77.92% and 83.14%, respectively. (2) Non-metallic minerals and other mining products underwent the largest decline in water lock-in within the sectors, whereas coal, oil and gas extraction products underwent the most significant decline in water lock-in between the sectors. (3) Water lock-in conduction is most durable and obvious from S01 (agriculture, forestry, fishery products and services) to S06 (textiles). Policy recommendations are suggested to realise the water-unlocking path.

It is of crucial importance to identify the driving factors for emission changes since China’s commitment to reduce carbon intensity in 2009. Hence, the spatial-temporal variation of carbon intensity of China’s 30 provinces from 2010 to 2017 is explored by applying a Spatial-temporal Index decomposition analysis (ST-IDA) model combined with energy input-output analysis. Industrial structure, energy intensity, energy structure, and carbon emission coefficient are identified as driving factors; simultaneously, a new factor, energy conversion efficiency, is also introduced based on the energy input-output analysis, which is of significance as China is vigourously pushing electricitification. The results show that the carbon intensity of economic sectors in most provinces declined from 2010 to 2017. Energy intensity is the biggest contributor to both the temporal decline of carbon intensity and its spatial difference for economic sectors, followed by industrial structure, energy conversion efficiency, energy structure and carbon emission coefficient, while the rank of inhibition of each factor is the same as above. Meanwhile, the carbon intensity of the residential sector is mainly affected by per capita GDP and per capita energy consumption. Related policy suggestions are given.

At present, the imbalance in regional development and carbon emissions are the two major challenges that China faces in terms of achieving high-quality development. This paper takes regional development differences as the starting point. First, we adopt the improved CRITIC method to measure the comprehensive development level of 30 regions in China and use K-means clustering to divide the 30 regions into five development levels. Second, the structural path analysis for environmental input–output analysis (EIOA-SPA) model is used to quantify the transfer of carbon emissions between sectors in various regions. Finally, a comprehensive analysis is performed based on the development characteristics of each region and the decomposition results of the carbon emission paths. Then, more precise carbon emission reduction strategies are proposed for the development of different regions in China. The results show that first, the development gap between regions in China has improved, and the development of the central and western regions has achieved remarkable results. However, differences between the north and the south and the gap between coastal and inland regions still exist. Second, the direct carbon emissions of regions with different levels of development are mainly derived from high energy-consuming sectors, especially the production and supply of electricity and heat sector. Third, there are certain differences in the indirect carbon emission pathways of regions with different development levels. The transportation, storage, and postal sector in high developed regions have obvious driving effects on carbon emissions. The building sector plays a prominent role in driving carbon emissions in high developed regions and medium–high developed regions. The building sector, nonmetallic mineral products sector, metal smelting sector, and rolled processed product sector in medium developed regions and medium–low developed regions have relatively high carbon emission-stimulating effects. Therefore, it is necessary to adopt differentiated emission reduction strategies for regions with different development levels in China to achieve adequate carbon emission reductions. This effort would further promote the construction of China’s ecological civilization.

Excessive carbon emissions from energy consumption seriously restrict China’s sustainable development and eco-environmental protection. Although the carbon emissions from the construction industry are less than that of the power, transportation, and manufacturing sectors, the carbon emissions released by the construction industry cannot be ignored due to its extensive development trend of high energy consumption and low efficiency. Based on this, this paper studies energy-related carbon emissions and emissions reduction of China’s construction industry from 2007 to 2017 by adopting the input–output analysis method, energy consumption method, and structural decomposition model. The results show that within the sample range: (1) The optimization of the construction industry energy consumption structure has a significant reduction effect on the growth of energy carbon emissions from the construction industry in China, and the reduction effect has shown an increasing trend over time. However, it should be noted that in this sample range, the optimization of energy consumption structure in the construction industry is mainly reflected in the decrease of the proportion of high-carbon energy consumption such as raw coal, while low-carbon energy such as natural gas has not played a significant role. Therefore, the future energy optimization space of China’s construction industry is still huge. (2) Energy intensity effect and input structure effect have a positive inhibitory effect on carbon emission growth of the construction industry, and the inhibitory effect of energy intensity effect is stronger than that of input structure effect. It shows that in the sample range, the generalized technological progress and energy efficiency of the construction industry have been better optimized and improved. (3) Except for 2015–2017, the final demand effect in other intervals has a positive effect on the growth of carbon emissions in the construction industry, and the secondary and tertiary industries play a major role in the final demand effect. It shows that the total demand for the construction industry in various industries still maintains a growth trend. This paper provides a theoretical analysis basis and practical guidance for China’s construction industry to carry out more accurate and efficient emission reduction from the supply-side energy varieties and demand-side industry level, and further enriches the existing research on carbon emissions of the construction industry from the perspective of input–output analysis.

Coastal cities play an important role in regional economic development and sustainable development strategies of water resources and their ecological environment. As a typical coastal city in Hebei Province, Qinhuangdao city is facing severe problems, such as water shortages and water environment deterioration, while social and economic development continues. Based on input-output analysis, we established a dynamic optimization model among Qinhuangdao city’s socioeconomic development, water resources and water environment. The 2013–2030 simulation after introducing comprehensive water resources policies of regional development, examines the regional socioeconomic development, optimizes the water resources supply structure and improves the water environment under the optimal scenario, and evaluates policy feasibility through cost-benefit analysis (CBA). The simulation results suggest that under the optimal scenario, the water-use efficiency (WE) of Qinhuangdao is improved by 59.14% and the proportion of reclaimed water and desalinated seawater in the water supply structure is increased by 13.70%. In addition, it has achieved an average annual gross regional production (GRP) growth rate of 6.36% and an average annual chemical oxygen demand (COD) emission rate of 17.95%. Moreover, the net present value (NPV) of the projects under the optimal scenario is 1.534 billion Chinese yuan (CNY), which means that the policy is economically feasible. Our research is helpful to improve the WE, optimize the water supply structure and protect the hydrogeological environment in coastal cities with water shortages and can provide a reference for Qinhuangdao city and other similar coastal cities to realize the rational utilization of water resources and regional sustainable development.

With rapid industrialization and urbanization, regional water shortages and water quality deterioration have posed great challenges for the sustainable development of cities in North China, especially those with a large demand for agricultural irrigation water. Based on an input-output analysis, this paper develops a dynamic optimization model consisting of three sub-models and multiple constraint conditions to solve the water crisis of Baoding, a typical city experiencing water shortages and serious water pollution in North China. The water resource carrying capacity (WRCC) indicator is introduced in the analysis of the results to comprehensively assess the effect of integrated water environmental policies (IWEPs) from 2013 to 2025. In the optimal scenario, the annual chemical oxygen demand (COD) discharge and annual water demand in Baoding can be reduced by 2.6% and 0.6%, respectively, with an annual gross regional product (GRP) growth rate of 7.52%. The WRCC can be improved from moderately overloaded to weakly unsaturated, which indicates that water resources can meet the socioeconomic development requirements. The results demonstrate the effectiveness of the linear optimization model with input-output analysis in coordinating the relationships among water demand, water environment protection, and economic development, and the IWEPs provide an applicable reference for decision-makers in Baoding and other similar cities in North China to address deteriorating water systems.

Indirect carbon emissions account for a large ratio of the total carbon emissions in processes to make the final products, and this implies indirect carbon emission flow across industries. Understanding these flows is crucial for allocating a carbon allowance for each industry. By combining input–output analysis and complex network theory, this study establishes an indirect carbon emission flow network (ICEFN) for 41 industries from 2005 to 2014 to investigate the interrelationships among different industries. The results show that the ICEFN was consistent with a small-world nature based on an analysis of the average path lengths and the clustering coefficients. Moreover, key industries in the ICEFN were identified using complex network theory on the basis of degree centrality and betweenness centrality. Furthermore, the 41 industries of the ICEFN were divided into four industrial subgroups that are related closely to one another. Finally, possible policy implications were provided based on the knowledge of the structure of the ICEFN and its trend.

China is committed to achieving the goals of “peak carbon and carbon neutrality,” and the carbon dioxide emissions generated in the energy utilization process mainly come from industrial and energy systems. This paper used structural decomposition analysis (SDA) and input-output analysis to study the structural emission reductions in China’s industrial and energy systems in 2007–2015. The results revealed that the final demand effect was the main factor promoting the growth of energy-related CO₂ emissions and that the energy intensity effect played a weak role in promoting the growth of energy-related CO₂ emissions. However, the energy structure effect and input structure effect reduced energy-related carbon emission growth. We found that for energy systems, the emission reduction effects of blast furnace gas, raw coal, refinery dry gas, and natural gas were obvious, while those of crude oil, gasoline, fuel oil, and kerosene were not obvious. For industrial systems, the tertiary industry played a major role in the final demand effect, followed by the secondary industry, and the primary industry in turn. This paper provides a theoretical basis and practical guidance for the carbon peak and carbon neutrality goals of China’s energy systems and industrial systems.