公众对电力来源清洁化的支付意愿

doi:10.18402/resci.2020.12.06

摘要/Abstract

摘要：

电力来源清洁化对于中国的环境保护和能源安全有重要意义。了解公众对电力来源清洁化的偏好及支付意愿可为政府制定能源改革政策提供支撑。本文采用离散选择实验法研究了公众对电力来源清洁化的偏好和支付意愿,分析了电力来源结构、环境影响（CO₂、SO₂排放量）、电价等属性对偏好的影响以及不同特征个体间偏好的差异。基于在中国10个主要城市收集到的1008份有效问卷,本文运用Mixed Logit模型和Latent Class模型分析了数据。结果发现：①公众对电力来源的偏好存在地域差异;②电力来源带来的环境影响以及电价的增加均与公众对电力清洁化偏好程度呈负相关;③不同特征的个体对电力来源的偏好也不同,年龄越大、收入越高、居住地越靠近城市和越重视环保的受访者,越偏好清洁的电力来源;④公众愿意为SO₂或CO₂排放量降低30%的清洁电力多支付31%的电价,公众的环保态度对该支付意愿的影响（差异为26%左右）大于社会经济特征（如年龄）对支付意愿的影响（差异为15%左右）。基于此,本文建议在各地采用精细化的政策促进电力来源清洁化,以及增强公众的环保意识,尤其是低收入群体、年轻群体和乡村群体,从而提高公众对清洁可再生能源的接受度和支付意愿。

关键词: 电力来源结构, 环境影响, 公众选择偏好, 离散选择实验, 异质性, 支付意愿, 潜在分类模型

Abstract:

Switching to cleaner power sources is important for China’s environmental protection and energy security. Understanding public preference for power sources and willingness to pay for cleaner power sources can provide information for the government to develop energy policies. This study used a discrete choice experiment to explore public preference and willingness to pay for cleaner power sources, analyzed the influence of attributes such as power source structure, associated environmental impacts (carbon dioxide and sulfur dioxide emissions) and electricity price change on preference, and characterized preference heterogeneities among different demographic and socioeconomic groups. A total of 1008 valid responses was collected in 10 selected cities, and mixed logit models and latent class models were used to analyze the data. The following results are obtained. (1) The public in different regions have different preferences for power sources. (2) Environmental impacts associated with the power sources and increase in electricity prices are negatively associated with public preference for cleaner power sources. (3) Preferences for power sources are different among different groups of people. Those who are older, with higher income, living closer to the city, and with pro-environmental attitudes prefer cleaner power sources. (4) The public is willing to pay 31% more for cleaner power sources that reduce sulfur dioxide or carbon dioxide emissions by 30%. The impact of public environmental attitude on the willingness to pay (the difference is about 26%) is greater than socioeconomic characteristics such as age (the difference is about 15%). Based on these findings, we suggest that precise policies be designed across regions, and efforts be put on raising public environmental awareness, especially for those with lower income, are younger, and live in rural areas, to improve public acceptance of and willingness to pay for renewable energy.

Key words: power source structure, environmental impact, public preference, discrete choice experiment, heterogeneity, willingness to pay, latent class model

刘晓, 徐建华. 公众对电力来源清洁化的支付意愿[J]. 资源科学, 2020, 42(12): 2328-2340.

LIU Xiao, XU Jianhua. Public willingness to pay for cleaner power sources[J]. Resources Science, 2020, 42(12): 2328-2340.

图/表 11

表1

图1

表2

表3

电力来源属性平均值及个体特征对偏好影响的结果"

	模型1	模型2	模型3	模型4	模型5	模型6
水电	-0.123	-0.156**	-0.135*	-0.151**	-0.150	-0.070
可再生能源电力	0.030	-0.000	0.010	0.003	0.022	0.017
均衡电力	-0.321***	-0.350***	-0.337***	-0.347***	-0.368***	-0.303***
核电	-0.088	-0.138*	-0.099	-0.105	-0.150	-0.005
电价	-3.668***	-6.270***	-4.645***	-4.154***	-7.597***	-4.065*
SO₂	-4.148***	-4.632***	-2.920***	-3.613***	-2.263***	-2.784***
CO₂	-3.541***	-2.941***	-2.025***	-3.195***	-1.455*	-3.931***
电价-居住地	—	-1.404**	—	—	—	—
电价-收入	—	0.232**	—	—	—	—
CO₂-居住地	—	0.094	—	—	—	—
CO₂-收入	—	-0.063	—	—	—	—
SO₂-居住地	—	0.384*	—	—	—	—
SO₂-收入	—	-0.057	—	—	—	—
电价-年龄	—	0.002	—	—	—	—
CO₂-年龄	—	-0.018**	—	—	—	—
SO₂-年龄	—	0.001	—	—	—	—
电价-学历	—	0.527**	—	—	—	—
CO₂-学历	—	0.070	—	—	—	—
SO₂-学历	—	0.067	—	—	—	—
电价-气候变化重视度	—	—	0.096	—	—	—
CO₂-气候变化重视度	—	—	-0.390***	—	—	—
SO₂-气候变化重视度	—	—	-0.127	—	—	—
电价-大气污染重视度	—	—	0.161	—	—	—
CO₂-大气污染重视度	—	—	0.045	—	—	—
SO₂-大气污染重视度	—	—	-0.159	—	—	—
电价-是否患哮喘	—	—	—	0.647	—	—
CO₂-是否患哮喘	—	—	—	0.038	—	—
SO₂-是否患哮喘	—	—	—	-0.641	—	—
电价-是否戴口罩	—	—	—	0.610**	—	—
CO₂-是否戴口罩	—	—	—	-0.138	—	—
SO₂-是否戴口罩	—	—	—	0.092	—	—
电价-查AQI频率	—	—	—	-0.441***	—	—
CO₂-查AQI频率	—	—	—	-0.004	—	—
SO₂-查AQI频率	—	—	—	-0.009	—
电价-感知空气改善	—	—	—	—	0.239	0.161
CO₂-感知空气改善	—	—	—	—	-0.144	0.141
SO₂-感知空气改善	—	—	—	—	-0.008	0.155
电价-发展可再生能源政策支持度	—	—	—	—	0.361	—
CO₂-发展可再生能源政策支持度	—	—	—	—	-0.290	—
SO₂-发展可再生能源政策支持度	—	—	—	—	-0.316*	—
	模型1	模型2	模型3	模型4	模型5	模型6
电价-对政府信任度	—	—	—	—	0.426	0.606
CO₂-对政府信任度	—	—	—	—	-0.126	0.000
SO₂-对政府信任度	—	—	—	—	-0.116	-0.085
电价-碳减排支持度	—	—	—	—	—	-0.501
CO₂-碳减排支持度	—	—	—	—	—	-0.005
SO₂-碳减排支持度	—	—	—	—	—	-0.354*
标准差	—	—	—	—	—	—
电价	3.228***	2.859***	2.997***	3.132***	-2.997***	-3.407***
SO₂	2.088***	1.995***	1.969***	2.066***	1.849***	2.004***
CO₂	1.861***	1.796***	1.694***	1.814***	1.604***	1.923***
样本量	32256	31424	32224	31968	16384	15680

表3

表4

图2

表5

表6

表7

表8

表9

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属性	水平
电力来源组合	（基准-火电）/水电/核电/风电和太阳能发电/ 均衡电力
气候变化相关物质的排放（CO₂）	+70% / +30% / 相比现状不变 / -30% / -70%
会影响健康的物质的排放（SO₂）	+70% / +30% / 相比现状不变 / -30% / -70%
电价	+20% / +10% / 相比现状不变 / -10% / -20%

城市	区域	污染天数	波动情况	空气质量分类	设定样本量	有效样本量
北京	华北	106(14^th)	71(6^th)	高污染,波动大	100	105
乌鲁木齐	西北	72(33^rd)	59(17^th)	高污染,波动大	100	102
哈尔滨	东北	67(41^st)	73(4^th)	高污染,波动大	100	106
西安	西北	44(72^nd)	38(78^th)	中等污染,波动较大	100	92
成都	西南	43(78^th)	39(72^nd)	中等污染,波动较大	100	102
上海	华东	38(94^th)	34(113^rd)	中等污染,波动较小	100	105
重庆	西南	35(103^rd)	34(117^th)	中等污染,波动较小	100	95
银川	西北	25(139^th)	31(136^th)	轻污染,波动小	100	96
兰州	西北	12(180^th)	21(205^th)	轻污染,波动小	100	107
广州	华南	7(199^th)	20(217^th)	轻污染,波动小	100	98
总计	—	—	—	—	1000	1008

地区	2016年GDP/亿元	经度/°	纬度/°	环境质量综合指数
北京	25669.13	116.40	39.90	6.75
哈尔滨	6101.61	126.53	45.80	6.28
上海	28178.65	121.47	31.23	4.66
广州	19547.44	113.27	23.13	4.81
重庆	17740.59	106.55	29.57	5.28
成都	12170.23	104.07	30.67	6.37
西安	6257.18	108.93	34.27	8.24
兰州	2264.23	103.82	36.07	6.48
银川	1617.71	106.28	38.47	7.00
乌鲁木齐	2458.98	87.62	43.82	7.56

城市	分组数	对数似然函数	一致赤池信息量	贝叶斯信息量
北京、哈尔滨	2	-398.781	969.062	942.062
北京、哈尔滨	3	-375.909	1037.651	992.651
上海、广州、成都、重庆	2	-824.155	1837.080	1810.080
	3	-799.630	1913.876	1868.876
	4	-768.412	1977.287	1914.287
西安、银川、兰州、乌鲁木齐	2	-449.214	1068.884	1041.884
西安、银川、兰州、乌鲁木齐	3	-424.812	1133.719	1088.719

变量	Ⅰ类地区		Ⅱ类地区		Ⅲ类地区
变量	1组	2组	1组	2组	1组	2组
属性变量
电价	-4.114***	-2.256***	-1.214***	-6.668***	-5.761***	-3.715***
CO₂	-3.813***	-3.738***	-6.537***	-1.467**	-6.835***	-2.626***
SO₂	-2.422***	-7.240***	-6.846***	-4.299***	-5.601***	-3.305***
水电	-0.389	-0.0781	0.173	-0.433	0.140	-0.272
可再生能源电力	-0.319	0.864**	0.148	-0.088	0.173	-0.513**
均衡电力	-0.839***	0.546	0.166	-1.028***	0.759*	-0.665***
核电	-0.273	0.388	0.300	-0.525*	0.230	-0.454
个人特征向量
收入	-0.514	0	0.213	0	0.893**	0
居住地	-14.830	0	0.464	0	0.270	0
年龄	-0.103*	0	-0.007	0	0.067	0
学历	-0.017	0	-0.400	0	0.505	0
气候变化重视度	-0.193	0	-0.091	0	2.168*	0
大气污染重视度	-0.207	0	0.119	0	1.361**	0
检查AQI频率	1.185**	0	-0.262	0	0.674	0
发展可再生能源政策支持度	-2.708**	0	0.910**	0	-1.814**	0
常数项	28.290	0	-1.908	0	-17.260	0
各组占比	0.426	0.574	0.724	0.276	0.536	0.464