Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China

Abstract

Addressing climate change and air pollution goals in conjunction would be efficient and cost-effective. Dealing with these two challenges is a common issue for urban clusters pursuing sustainable development. Expected to become the fourth international first-class bay area, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) takes the lead in exploring a green and low-carbon transition path as a key element of being a pioneering economic reform demonstration zone. Based on an integrated modeling framework that couples an energy-economy model (IMED|CGE), decomposition analysis, and regression methods, the potential contribution of achieving the climate mitigation target to air pollutant reduction in the GBA by 2050 was quantified. The results showed that the transport sector has the most significant potential for carbon reduction. Energy intensity and structural transformations are the main contributors to reducing carbon emissions, with the latter becoming increasingly important over time. Climate policy can effectively reduce air pollutant emissions; however, this effect varies for different pollutants and sectors. Based on the assessment of the synergy index and cost of abatement, sectors with priority for synergic governance were identified. The regression results indicated that the carbon shadow price would be significantly more effective in reducing air pollutant emissions in the post-2030 period than before 2030, except for SO _2 and NH _3 , partially because of the existing actions that cause the synergistic effects to decline. In addition, end-of-pipe removal measures still play a relatively significant role in reducing air pollutants, particularly VOC, NH _3, and primary PM _2.5 . Thus, the findings suggest that priority should be given to sectors with huge synergistic benefits, such as transportation and power generation while paying attention to possible trade-offs.

Keywords

• 1.5 °C target
• 2 °C target
• air pollutant
• IMED|CGE model
• decomposition analysis
• regression model