湖北省交通业碳排放影响因素及情景预测

CARBON EMISSION FACTORS AND SCENARIO FORECAST OF HUBEI’S TRANSPORTATION INDUSTRY

论文选取城镇化率、客货运周转量、人均GDP和能源强度4个指标反映湖北省人口、经济和技术水平对湖北省交通业碳排放的影响。首先对湖北省碳排放情况和人口城镇化比例及能源强度进行整体的分析,然后运用STIRPAT扩展模型对湖北省交通业碳排放影响因素进行研究。在研究过程中,发现数据存在明显的多重共线性问题,此时普通的最小二乘法得到的结果不准确,故使用偏最小二乘法来消除解释变量之间的多重共线性。并使用留一法交叉验证得到的结果是否合理,最后对数据进行偏最小二乘法两次迭代映射分解得到碳排放量与其影响因素之间的系数。对结果进行显著性检验,验证了结果的可靠性,结果表明城镇化率、客货运周转量、人均GDP和能源强度4个指标均与交通业碳排放总量呈正相关关系。在此基础上根据2000—2018年的湖北省交通业碳排放数据,使用情景分析法设定低、中、高3种碳排放情景对2030年碳排放量进行预测。首先计算出3种不同情景下不同解释变量的年平均增长率,然后将结果带入碳排放量与解释变量之间的公式中计算得出2019—2030年碳排放量的年平均增长率,由此得出2030年的碳排放量在低排放情景、基准情景和高排放情景下分别为1 081.772万t、1 131.407万t和1 176.507万t。分析显示人口因素和技术因素对交通业碳排放量影响最大,城镇化率的提高和能源消耗强度的增强会导致能源需求量和碳排放量大幅度增加,加大了城市交通运行压力。结合2030年前完成碳峰值的目标,将3种情景下湖北省交通业碳排放量预测结果与目标进行对比,显示均完成目标达到碳排放峰值。最后针对影响碳排放的相关因素提出相关节能减排建议,以期优化交通能源结构,实现低碳经济和可持续发展目标。

Urbanization rate, passenger and freight turnover, per capita GDP and energy intensity are used to mark the carbon emission impacts of Hubei’s population, economy and technologies on its transportation industry. This paper analyzes the carbon emission, population urbanization rate and energy intensity, and uses STIRPAT extended model to study carbon emission factors of Hubei’s transportation industry. Data exists lots of multiple co-linear issues that make inaccurate results if ordinary least method is used. After processed with partial least square method, a coefficient of carbon emission and factors is obtained. The results show that urbanization rate, passenger and freight turnover, per capita GDP and energy intensity are positively connected with the gross carbon emission by transportation industry. According to Hubei’s 2000—2018 carbon emission data of transportation industry, scenario method is employed to forecast the 2030 carbon emission, low, middle and high carbon emission. Under these three scenarios, annual growing rate of explained variables are calculated, its results then are used to estimate the annual average growing rate of 2019 to 2030 carbon emission. In 2030 carbon emission will be 10 817 kt, 11 314 kt and 11 765 kt under low carbon emission scenario, benchmark scenario and high scenario. Population and technology play a bigger part in carbon emission, increment of urbanization rate and energy intensity will result in an large increase in energy demand and carbon emission, exerting pressure on urban transportation. Combined with state’s 2030 carbon emission peak objectives, and compared with 2030 forecasted results on three scenarios, the carbon emission peak is reached. This paper presents suggestions on saving energy and reducing carbon emission, optimizing transportation energy structure and conducting low-carbon economy and sustainable development policies.