Quantifying the synergy and trade-offs among economy-energy-environment-social targets: A perspective of industrial restructuring.

Protecting our environment while maintaining economic growth, requires a delicate balance among interlinked sustainable development policies. In this paper, we examine China's economic industries, including a high-resolution of the country's electricity sector during 2020-2030, using a multi-objective optimization model based on Input-Output analysis. This model, investigates the synergy and trade-offs of sustainable development goals in maximizing employment and GDP while minimizing energy and water consumption, CO2 emissions, and five major pollutants to advance a sustainable industrial structure adjustment pathway for China. Our results reveal that there exists both synergies and trade-offs among multiple objectives, e.g., synergy among goals of minimizing air pollutant emissions and trade-offs between minimizing energy consumption and maximizing employment. Through the planned industrial restructuring period (2020-2030), the GDP, employment, carbon emission, and energy consumption will increase respectively by, 96.1%, 7.2%, 16.8%, 16.8%, and 6.3%, while pollutant emissions would decrease. Moreover, our research indicates that energy and water conservation should be prioritized in industrial structure adjustment strategies and policies. Our model demonstrates how the synergies and trade-offs among multiple policy targets can empower policy-makers, especially in developing nations, to make more informed and optimized industrial structure adjustment policies for sustainable development.

Socio-economic drivers of rising CO2 emissions at the sectoral and sub-regional levels in the Yangtze River Economic Belt.

As the world's largest inland shipping channel, the Yangtze River Economic Belt (YREB) is strategic to China's sustainable development where policymakers are increasingly emphasizing not only this region's economic development but also its CO2 emission reduction targets. To achieve emission targets in the YREB region, it is essential to identify the driving forces of its CO2 emissions. However, existing studies are not very refined and only examine the overall effects of drivers on CO2 emission changes, while neglecting the sub-regional and sectoral level effects across China. More refined research, therefore, will provide better-targeted policies for emission reduction relevant to regional levels such as the YREB region. Towards this end, this paper integrates the methods of structural decomposition analysis and attribution analysis to demonstrate the driving forces, at both sub-regional and sectoral levels, for YREB's emission changes from 2002 to 2012. Our results reveal the following: (1) Jiangsu Province has always been the main regional source of increasing CO2 emissions in the YREB, accounting for more than 20% of total CO2 emission growth. (2) The Electricity and Heat Production and Supply sector is responsible for most of the increases in CO2 emissions, both in 2002-2007 (609.8 Mt, 54.8%) and 2007-2012 (287.6 Mt, 34%). (3) During the period of 2007-2012, changes in per capita final demand were the primary driving force for the increases in CO2 emissions, while changes in CO2 emission intensity were the largest driving force for decreasing CO2 emissions, respectively accounting for 179.9% and -119.4% of total emission changes in the YREB region. (4) Moreover, the effect of emission intensity mainly exists in the Electricity and Heat Production and Supply sector in the Jiangsu and Zhejiang provinces, accounting for respectively, 10% and 10.4% of the total effects across all economic sectors. Considering the diverse impacts of driving forces in different sub-regions and economic sectors, policymakers should apply more refined measures to utilize varying driving forces in different sub-regions and economic sectors towards sustainable development.

Decreasing resilience of China's coupled nitrogen-phosphorus cycling network requires urgent action.

The coupled nature of the nitrogen (N) and phosphorus (P) cycling networks is of critical importance for sustainable food systems. Here we use material flow and ecological network analysis methods to map the N-P-coupled cycling network in China and evaluate its resilience. Results show a drop in resilience between 1980 and 2020, with further decreases expected by 2060 across different socio-economic pathways. Under a clean energy scenario with additional N and P demand, the resilience of the N-P-coupled cycling network would suffer considerably, especially in the N layer. China's socio-economic system may also see greater N emissions to the environment, thus disturbing the N cycle and amplifying the conflict between energy and food systems given the scarcity of P. Our findings on scenario-specific synergies and trade-offs can aid the management of N- and P-cycling networks in China by reducing chemical fertilizer use and food waste, for example.

An energy system optimization model accounting for the interrelations of multiple stochastic energy prices.

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein-Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-021-04229-3.

Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012.

The resilience of the phosphorus (P) cycling network is critical to ecosystem functioning and human activities. Although P cycling pathways have been previously mapped, a knowledge gap remains in evaluating the P network's ability to withstand shocks or disturbances. Applying principles of mass balance and ecological network analysis, we examine the network resilience of P cycling in China from 1600 to 2012. The results show that changes in network resilience have shifted from being driven by natural P flows for food production to being driven by industrial P flows for chemical fertilizer production. Urbanization has intensified the one-way journey of P, further deteriorating network resilience. Over 2000-2012, the network resilience of P cycling has decreased by 11% owing to dietary changes towards more animal-based foods. A trade-off between network resilience improvement and increasing food trade is also observed. These findings can support policy decisions for enhanced P cycling network resilience in China.