Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems.

Carbon reduction is imperative for achieving carbon peaking and neutrality. Accordingly, it is important to determine which industrial sectors have more responsibility in this matter. Based on data from Zhejiang\'s input-output tables, this study applies the Input-Output method to measure and compare the carbon emissions of 42 industrial sectors in Zhejiang Province from 2002 to 2017, and then assesses the carbon emission attributes of each industrial sector, and ultimately determines the responsibility for carbon emission reduction from the perspectives of the producers and consumers. The results of the study show that direct carbon emissions and whole-process carbon emissions in Zhejiang increased continuously from 2002 to 2017, with carbon emission intensity first decreasing and then increasing. However, carbon emission intensity was much lower in 2017 than in 2002. Over time, the attributes of carbon emissions by sector changed little. Particularly, high-carbon sectors covered most of the energy supply sectors, low-carbon sectors were mostly tertiary-related, and pseudo-low-carbon sectors were mainly found in the productive services sector. In terms of carbon emission reduction responsibilities, there are large differences in emission reduction responsibilities between sectors, with the electricity and heat sectors bearing the largest responsibilities based on their production, consumption and total carbon emission reductions. The conclusions of this study can provide some data support for the further development of carbon peak and carbon neutral plans.

Past studies related to embodied pollutant accounting reported that free trade has increased the environmental pollution of developing economies, because the developed countries \"outsource\" their pollutants to developing nations. The COVID-19 pandemic has stimulated the rise of the most serious protectionism after World War II. This study is aimed to discuss whether protectionism improve the environment in developing countries by developing a comprehensive evaluation model, which integrates multi-regional input-output (MRIO), data envelopment analysis (DEA), and scenario analysis. We revealed the role of protectionism from two perspectives: the single impact on pollutant emissions and the comprehensive impact on environmental efficiency. Specifically, the capital inputs, labor inputs, energy consumption, economic output, carbon dioxide, sulfur dioxide and nitrogen oxides emissions related to global trade activities were simulated based on the MRIO. And then, sector-level trade environmental efficiency was computed by intergrading the MRIO and DEA using a non-radial directional distance function. Finally, the environmental efficiency of both developing and developed countries under two scenarios with and without trade were estimated. The results confirmed that trade has increased the CO2, SO2 and NOX emissions of developing economies by 12.9%, 9.8% and 12.3%, and has reduced that of developed economies by 6.0%, 29.4% and 21.2%, respectively. However, the results also uncovered that the environmental efficiency of developing and developed economies was dropped by 3% and 5%, respectively, under no-trade scenario. We contend that protectionism is not conducive to the sustainable development of developing countries because it lowers their environmental efficiency, although it may reduce their territorial pollutant emissions. For developed countries, the single impact of protectionism on pollutant emission reduction and the comprehensive impact on environmental efficiency are both negative.

As the division of work within the world economic system becomes increasingly complex, the impact of disturbing events on the economic system is expanding. Recently, Japan proposed to discharge nuclear wastewater into the Pacific Ocean, which will cause damage to marine fisheries, thereby seriously affecting fisheries and other industries in Japan and other countries and regions around the world. Considering different scenarios of final and intermediate demand shifting, this paper uses the Inoperability Input-Output Model (IIM) and Multi-Region Input-Output Model (MRIO) to simulate the economic consequences of nuclear wastewater discharge in Japan and calculate the economic changes of each industry and country (region). The results show that: In the short term, when only the final demand for Japanese fishery products decreases. (1) The ten countries (regions) with significant economic losses are Japan, the United States, Chinese Taipei, Canada, Chile, South Africa, Mexico, Peru, the United Kingdom, and Ireland. (2) The ten countries (regions) with a significant increase in total output due to demand shift are China (People\'s Republic of), the Rest of the World, India, Indonesia, Viet Nam, the Philippines, Brazil, Myanmar, the Russian Federation, and Malaysia. (3) A ranking of changes in the total output of different industries. In the long term, when both intermediate and final demand for Japanese fishery products decrease. (4) The change in value added in Japan. (5) The change in value added of 67 countries (regions) worldwide. The ten countries (regions) with the most significant increase in value-added are the Russian Federation, China (People\'s Republic of), the Rest of the World, the United States, Indonesia, Australia, Norway, Korea, Viet Nam, and Myanmar. The ten countries (regions) with the most significant decrease in value-added are Japan, Chinese Taipei, Chile, South Africa, Peru, Thailand, Mexico, Cambodia, Costa Rica, and Morocco. Changes in value added of 45 industrial sectors worldwide.

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies. This article proposes a novel multiobjective mixed-integer linear programming formulation, which results in the optimal timing of closure and reopening of states and industries in each. The three objectives being pursued include: (i) the epidemiological impact of the pandemic in terms of the percentage of the infected population; (ii) the social vulnerability index of the pandemic policy based on the vulnerability of communities to getting infected, and for losing their job; and (iii) the economic impact of the pandemic based on the inoperability of industries in each state. The proposed model is implemented on a dataset that includes 50 states, the District of Columbia, and 19 industries in the United States. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction.

China is promoting the coordinated development of the Beijing-Tianjin-Hebei region as a national strategy project; however, water scarcity and water quality problems will become a bottleneck restricting high-quality development. This study aimed to explore a feasible industrial synergy optimisation pathway to realise the collaborative development of economic growth and water environment improvement, combined with incentives for environmental efficiency improvement and reclaimed water utilisation. Research methods integrate input-output modelling, system dynamics, and multi-objective programming to construct a complex multi-region model. A dynamic simulation measure was adopted to simulate the economic and environmental impacts of different approaches that mix from 2020 to 2030 under water resource environment constraints. According to the simulation results, the annual economic growth rate of the entire region can exceed 6.1%, and the emission intensities of water pollutants decrease by more than 60.0%. In addition, traditional manufacturing industries that achieve cross-regional synergy can still release location advantages without negative environmental impacts. Furthermore, regional collaborative development optimises the allocation of water resources and alleviates water stress. Moreover, the pollutant emission reduction effect of source control in Hebei was more effective than in other cities. Finally, reclaimed water, as the end treatment measure, has the largest marginal effect on improving the trade-off between economic and environmental improvement in the long run. This study provides a new approach for multi-regional industrial synergy development and optimal allocation of resources and contributes to the high-quality development of the watershed.

Analyzing the factors that affect the spatial differences in production water consumption in China is necessary to implement its most stringent water resource management system effectively. Based on the input-output tables of 31 provinces and the water-consumption data of provinces by sectors in 2017, the water consumption-economy input-output tables of 31 provinces are constructed. The spatial structural decomposition analysis method is used to analyze the impact of technology level, economic scale, and regional characteristics on spatial differences in production water consumption. The final demand effect is then decomposed into final demand sectoral structural effect, final demand distribution structure effect, population-scale effect, and consumption-level effect. The results show that production water consumption depends on the economic scale and regional characteristics. Xinjiang, Jiangsu, Guangdong, Heilongjiang, and most provinces in the central region use more production water than the average level, while those in the Beijing-Tianjin region and most in the North-west region use less than average. The decomposition results show that the technical and the final demand effects are the main factors for the spatial differences. The impact of population-scale and consumption-level contribute the most to the final demand effect.

Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations. Here we develop a machine learning-augmented method to improve the accuracy of the prediction of IO tables using the US summary-level tables as a demonstration. The model is constructed by combining the RAS method with a deep neural network (DNN) model in which the RAS method provides a baseline prediction and the DNN model makes further improvements on the areas where RAS tended to have poor performance. Our results show that the DNN model can significantly improve the performance on those areas in IO tables for short-term prediction (one year) where RAS alone has poor performance, R2 improved from 0.6412 to 0.8726, and median APE decreased from 37.49% to 11.35%. For long-term prediction (5 years), the improvements are even more significant where the R2 is improved from 0.5271 to 0.7893 and median average percentage error is decreased from 51.12% to 18.26%. Our case study on evaluating the US carbon footprint accounts based on the estimated IO table also demonstrates the applicability of the model. Our method can help generate timely IO tables to provide fundamental data for a variety of environmental footprint analyses.

In the current context of rising trade protectionism, deeply understanding the impacts of COVID-19 on economy and energy has important practical significance for China to cope with external shocks in an uncertain environment and enhance economic resilience. By constructing an integrated economic and energy input-output model including the COVID-19 shock, this paper assesses the impacts of COVID-19 on China\'s macro-economy and energy consumption in the context of trade protectionism. The results are shown as follows. First, in the context of protectionism, the outbreak of COVID-19 in China would cause a 2.2-3.09% drop in China\'s GDP and a 1.56-2.48% drop in energy consumption, while adverse spillovers from global spread of COVID-19 would reduce its GDP by 2.27-3.28% and energy consumption by 2.48-3.49%. Second, the negative impacts of domestic outbreak on China\'s construction, non-metallic mineral products, and services would be on average 1.29% higher than those on other industries, while the impacts of global spread of COVID-19 on export-oriented industries such as textiles and wearing apparel would be on average 1.23% higher than other industries. Third, the effects of two wave of the pandemic on China\'s fossil energy consumption would be on average 1.44% and 0.93% higher than non-fossil energy consumption, respectively.

Environmental pollution in the Pearl River Delta (PRD) region is largely driven by socioeconomic forces outside the region as vast majority of manufacturing products produced in the region are destined to national and international markets. Given the remarkable economic transformation of the PRD in the past decades, this study investigates the impacts of local, provincial, national, and global socio-economic drivers on PRD\'s pollution dynamics under the background of significant economic restructuring and upgrading from 1987 to 2017. The results indicate that changes in pollution pattern were deeply shaped by the economic transformation. The share of PRD\'s emissions driven by international exports expanded significantly before 2007 as a result of the fast growth of international exports. The transformation of economic growth to domestic consumption driven model since the 2007-2008 global financial crisis had resulted in an increasing contribution share to the PRD\'s environmental pollution from local demand and trade with Rest of China (RoC). Similarly, as final demand structure evolving towards the high value-added manufacturing and services, the share of emissions driven by low value-added manufacturing (LVM) demand had decreased by an enlarged margin, while that driven by high value-added manufacturing (HVM) demand and services demand had moved in the opposite direction. The structural decomposition analysis shows that reduction in emission intensity remains the most effective way in pollution alleviation. The contribution of changes in production input structure also shifted from a strong impetus force before 2007 to a mitigating force afterwards due to significant technological progresses in the industrial sectors since the global financial crisis. With the marginal cost of reducing emission intensity becoming prohibitively expensive, the optimization of production structure and consumption pattern is likely to play more important role in future emission mitigation.