王科教授与博士生王家钰、魏一鸣教授合作的论文How to balance China’s sustainable development goals through industrial restructuring: A multi-regional input–output optimization of the employment–energy–water–emissions nexus近日在线发表于国际期刊《Environmental Research Letters》。

　　为促进经济转型和可持续发展，中国政府在“十三五”（2016-2020年）规划中制定了一系列政策目标，涉及社会保障、经济增长、能源转型、资源节约和环境保护。考虑到中国的经济发展仍主要由工业生产推动、工业生产仍需大量以化石燃料为主的能源消耗，在维持经济平稳发展的前提下实现保证就业、降低能耗及污染排放等可持续发展目标面临巨大挑战。由于各可持续发展政策之间可能存在的竞争型和冲突性，如何选择一项具有优先性的政策，使在该政策指导下的产业结构调整路径能够平衡其他可持续发展目标成为了实现可持续发展的关键问题。

　　为了在各项“十三五”政策目标间取得平衡，我们建立了基于多区域投入产出分析的多目标优化模型（如Figure 1所示）。由于传统的多目标模型求解方式存在假设过强的弊端，即通过简单的假设确定权重把多目标模型转换为单目标模型；以及基于如遗传算法、粒子群算法、蚁群算法等的计算机算法的优化过程无法获知。本研究采用了首先求解各单目标规划模型，然后通过闵可夫斯基距离计算折中解的方式求解多目标规划问题，使得每步优化过程透明化。

Figure 1Framework of the multi-objective optimization model based on multi-regional input–output analysis for balancing sustainable development goals

　　应用该模型评估上述几种政策主导的产业结构调整路径对就业、能耗、水资源使用、碳排放和污染排放的协同（synergies）或权衡（trade-offs）影响（如Figure 2所示）。通过比较几种产业结构调整路径的政策一致性、趋势顺应性以及区域公平性，确定最优的产业结构调整路径以及最具优先性的政策。政策一致性指某政策主导的产业结构调整路径是否与其他政策目标的实现方式一致，即某政策主导的产业结构调整路径是否可以促进其他政策目标的协同实现。趋势顺应性指某政策主导的产业结构调整路径是否顺应国家产业结构转型方向。区域公平性指某政策主导的产业结构调整路径是否降低区域间不公平发展的程度。

　　结果表明：节能政策主导的产业结构调整路径是最优方案。在节能政策目标的主导下，通过产业结构调整的方式，能够最大程度的实现其他可持续发展目标的协同实现（如Figure 3所示），促进（抑制）高附加值（高能耗和高排放）行业的发展（如Figure 4所示），提高区域发展公平性（如Figure 5所示）。由此，为实现可持续发展，我们建议在制定产业结构调整路径时应优先考虑能源政策。该模型框架从政策制定的角度为决策者提供了实现可持续发展的方法。

Figure 2Synergies and trade-offs among various objectives. In this figure, environmental pollutant emissions are divided into air pollution (SO2, NOx, and SD) and water pollution (COD and AN).

原文获取链接：https://iopscience.iop.org/article/10.1088/1748-9326/ab666a/meta

Figure 3Radar diagram of each objective in the (a) baseline scenario, (b) employment-dominated scenario, (c) energy-consumption-dominated scenario, and (d) carbon-emission-dominated scenario. Absolute amounts are transformed to indexes, with targets equal to 1. The employment index is calculated by dividing the policy target by the absolute amount, while other indexes are measured by dividing the absolute amounts by the policy targets. The dotted line denotes the policy target standard; the solid line represents the objectives of single-objective solutions; while the filled area depicts the objectives of the compromise solution. Here, pollutant emissions contain only the five major environmental pollutants. The objective inside the target area is satisfactory; otherwise, the objective needs to be added in the constraints. Additionally, a bar chart of employment for the compromise solution in different scenarios is illustrated in (e). Detailed data on total outputs and the five objectives of each sector in each province are added in “Supplemental Data.xlsx”, which can be used as a reference for industrial restructuring for each province.

Figure 4Percentage changes of sectoral total outputs under different scenarios, compared with the baseline scenario. The codes and the corresponding sectors are provided in Table S.1.

Figure 5Diagram of the change in total outputs by province under the (a) employment-dominated scenario, (b) energy-consumption-dominated scenario, and (c) carbon-emission-dominated scenario. Changes within -1% ~ 1%, higher than 1%, and lower than -1% are defined as constant, increasing, and decreasing, respectively. Grey areas in the first three subgraphs are not analyzed due to unavailability of data. The fourth subgraph (d) is the Chinese regional map marked with the provinces’ names.