Key Laboratory for Geographical Process Analysis & Simulation Hubei Province, Central China Normal University, Wuhan 430079, China.
School of Economics and Management, Southwest University, Chongqing 400715, China.
Int J Environ Res Public Health. 2020 Feb 12;17(4):1159. doi: 10.3390/ijerph17041159.
Promoting tourism in China using sustainable practices has become a very important issue. In order to analyze temporal characteristics and spatial regularities of green total factor productivity (GTFP), carbon emissions and the consumption of energy related to tourism in China were estimated using a "bottom-up" method. The construction of a measurement framework (including carbon emissions and energy consumption) of GTFP for the tourism industry was also undertaken. The data envelopment analysis (DEA) model and the Malmquist-Luenberger (ML) index were used to measure and calculate tourism GTFP in China between 2007 and 2018, as well as analyze spatio-temporal differences. Results indicate that: (1) carbon emissions and the consumption of energy are increasing, and they have not yet peaked, with traffic associated with tourism accounting for the largest proportion among tourism sectors; the spatial distribution of carbon emissions and the consumption of energy is not balanced; (2) green development of tourism in China has achieved a good level of performance during the study period, driven by technical efficiency. Since 2014, pure technical efficiency (PE) has been >1, indicating that the tourism industry in China has entered a stage of change and promotion; (3) significant spatial differences exist in tourism GTFP in China. For example, the overall pattern of being strongest in the east and weakest in the west has not changed. Currently, eastern, central, and western regions in China rely on different dynamic mechanisms to promote tourism green development. In addition, some provinces have become the core or secondary growth poles of tourism green development in China.
在中国,采用可持续实践来促进旅游业发展已成为一个非常重要的问题。为了分析绿色全要素生产率(GTFP)、碳排放和与旅游业相关的能源消耗的时间特征和空间规律,采用“自下而上”的方法对中国旅游业的 GTPP 进行了估计。还构建了旅游业 GTFP 的测量框架(包括碳排放和能源消耗)。使用数据包络分析(DEA)模型和 Malmquist-Luenberger(ML)指数来衡量和计算中国 2007 年至 2018 年的旅游业 GTFP,并分析时空差异。结果表明:(1)碳排放和能源消耗不断增加,尚未达到峰值,其中旅游交通占旅游业最大比例;碳排放和能源消耗的空间分布不平衡;(2)中国旅游业的绿色发展在研究期间取得了良好的绩效水平,这得益于技术效率。自 2014 年以来,纯技术效率(PE)>1,表明中国旅游业已进入变革和提升阶段;(3)中国旅游业的 GTPP 存在显著的空间差异。例如,东部最强、西部最弱的整体格局没有改变。目前,中国的东部、中部和西部地区依靠不同的动态机制来推动旅游业的绿色发展。此外,一些省份已成为中国旅游业绿色发展的核心或次增长极。