電動車政策與能源政策之環境綜效評估

Abstract

空氣汙染議題是近期各國致力於解決的問題，因為其會導致人體死亡與相關疾病之外部成本。其中又以PM2.5為民眾最關切之汙染物，因為PM2.5是心臟病、中風、慢性阻塞性肺病、肺癌、急性呼吸道感染等疾病的主要成因之一。因此，各國政府積極推動各項政策期望能夠降低空氣汙染的危害程度。在眾多防制空氣汙染的策略中，以移動汙染源與固定汙染源為兩大管制重點。在移動源管制的部分，電動車政策為近來國際上新興發展的策略，因為電動車除了能夠降低空氣汙染，更具備了促進能源效率、能源安全、減少噪音、降低溫室氣體排放之效果，同時在經濟發展上，電動車市場也具備了投資的潛力；而在固定源管制的部分，能源結構為國家能源政策中重要的一環，近年來又因為氣候變遷議題日益嚴峻，因此能源轉型(Energy Transition)為全球趨勢，各國政府都希望提高潔淨能源（如:再生能源、核能）佔國家發電的比例，以使國家走在低碳永續之道路上。 然而，縱然電動車政策與能源政策都具有降低空氣汙染的可能性，但若政府在評估政策時未考量多種政策的綜效性(Synergy)，有可能造成決策者未意料之結果。因為兩政策可能會相互加乘增進整體的福祉；抑或是，政策間的作用會相互抵減，削減各自的目標。 因此，本研究欲探討電動車政策與能源政策兩者相互作用下之綜效效果。研究方法以E3ME模型與FTT模型模擬政策於經濟圈內造成之作用與技術變動之效果，再以CMAQ模型與AERMOD模型結合汙染物抵換率模擬PM2.5於大氣中擴散之濃度分佈狀況，最後再以健康風險評估得出PM2.5對人體之慢性致死風險，以探討不同政策情境下之綜效效果。 研究結果顯示，在實施電動車政策的情境下，無論搭配上何種能源情境，其所造成之移動源減量效果皆遠大於其所造成之固定源增量效果，代表只要實施電動車政策，整體來說都具有空氣汙染減量的效益。同時，此結果在不同的空間尺度下皆是如此，代表著無論是以台灣尺度、縣市尺度或是鄉鎮區尺度，電動車政策都具有減量效果，並不會有地區出現固定源增量大於移動源減量的情況。接續上述結果，可以得知若實施電動車政策，會具有絕對的減量效果，但其對於各縣市之減量速度之變化，會隨著搭配的能源情境與不同年份而有所不同。例如減量效果前五大之縣市：高雄市、新北市、台中市、台南市以及台北市，在不同年份與不同能源情境之下，其減量速度之大小會有排序上的變動。整體而言，本研究之結果顯示出，在評估政策是否能達成預期效果時，需要評估相關政策的綜效性，同時考量不同年份的效果變化，方以在決策上具有更多資訊以作為參考依據。

Air pollution is a serious problem because it increases human mortality, respiratory diseases, and external costs. Among all the air contaminants, people concern PM2.5 the most since it causes heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and acute upper respiratory tract infection. As a result, governments have been putting efforts on making air pollution control policies. There are two kinds of air pollution control policies where one controls mobile sources and another controls stationary sources. To reduce mobile air pollution, publishing an electric vehicle policy is the emerging strategy used by many governments. This is because that electric vehicle not only reduces mobile air pollution, but also increases energy efficiency and energy security, and reduces noises and greenhouse gases. To reduce stationary air pollution, energy policies and energy structures are the two main focuses of many governments. Additionally, Energy Transition is a global trend since mitigating climate change is a global consensus. As a result, governments want to improve clean energy technologies to build a low-carbon economy. Despite electric vehicle policies and energy policies have the possibilities of reducing air pollution, there will be unexpected results if governments do not take synergistic effects on policies into consideration. Synergistic effects on policies might increase the total benefits of whole society. Or, two policies might interfere with each other and fail to reach the original targets. According to above reasons, this research focuses on the synergistic effects between electric vehicle policies and energy policies. The research method is described as follow. First, we simulated policy effects and technologies transformation on the whole economy by using E3ME model and FTT model. Second, we simulated the PM2.5 concentration distribution by using CMAQ model and AERMOD model combined with Pollutant Offset Ratio. Third, we calculated the risk of mortality by using human health risk assessment. Finally, we discussed the synergistic effects on policies under different scenarios. The result shows that, when electric vehicle policies are applied, the reduction of the risk of mortality caused by PM2.5 from mobile sources is much higher than the increasement of the risk of mortality caused by PM2.5 from stationary sources. The above statement is true no matter which energy scenario it is. Therefore, electric vehicle policies lead to a reduction of the risk of mortality. At the same time, no matter in which spatial scale (e.g. country scale, county scale and township scale), electric vehicle policies always bring positive effects. The significant difference is that the speed of reduction is different between counties and between years. For example, based on the current energy policies (N0R1), we compared the scenario that has electric vehicle policy with the one without electric vehicle policy, then we discovered that the top counties with highest risk reduction are: Kaohsiung City, New Taipei City, Taichung City, Tainan City and Taipei City. This ranking changes in other energy scenarios (N0R0/N1R0/N1R1). In conclusion, the result of this research suggests that decision makers should consider the policy synergistic effects and take both the time scale and the spatial scale into consideration to make more comprehensive policy decisions.