Regional co-control plan for local air pollutants and CO2 reduction: Method and practice

Journal of Cleaner Production(2017)

引用 40|浏览11
暂无评分
摘要
Improving air quality and at the same time mitigating climate change, has become a focal point around the world, especially for emerging countries like China. In the current government administration in China, the plans for local air pollutants and CO2 reduction are made separately and rely heavily on end-of-pipe measures. This type of stopgap strategy falls short of cost-effectiveness and reduction potential. Alternatively, a co-control planning method involving multi-pollutant co-reduction measures and least-cost optimization is proposed as a substitute solution. With a case study in Urumqi in Northwestern China, it is demonstrated that current local air pollutants and CO2 reduction plans are either too expensive due to overlapped finance budget and overuse of end-of-pipe measures or sometimes counter-affect each other. To use the proposed method to make an improvement, co-control measure options are subsequently reviewed and screened using a co-control effects coordinate system and an indicator of unit cost of pollutant reduction. The co-control plan can then be designed with a linear programing method. To evaluate the economic feasibility of the co-control plan, a cost-benefit analysis is conducted. The results indicate that the co-control plan has an obvious advantage over the current plan in terms of multi-pollutant co-reduction and cost effectiveness. A sensitivity analysis verifies the robustness of the co-control plan approach. This approach could serve as an effective way of simultaneous reduction of local air pollutants and CO2 with least cost. It is particularly useful for rapidly growing countries like China where both local air pollutants and CO2 emissions are very high. The co-control plan strategy can provide important environmental plan and decision making support.
更多
查看译文
关键词
Co-control,Local air pollutants,CO2,Environmental-economic evaluation,Cost-benefit analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要