中部六省城市旅游业碳排放时空演变分析

Analysis of the Spatial and Temporal Evolution of Carbon Emissions from Urban Tourism Industry in Six Central Provinces

摘要: 随着旅游业的发展，其产生的碳排放量越来越高，对全球的气候生态造成了威胁。本文选取中国中部六省为研究对象，采取“自上而下”的方法测算了该区域各市(州) 2014~2021年旅游业的碳排放量，运用slope趋势分析、探索性空间数据分析方法分别分析了中部六省各市(州)旅游业碳排放增长趋势和空间格局特征。结果表明：1) 中部六省2014~2019年大部分市(州)的旅游业碳排放呈现增长趋势，由于疫情影响，2019~2021年大部分市(州)的旅游业碳排放呈现下降趋势；2) 中部六省旅游业碳排放类型以中碳、较高碳和高碳为主，空间上变化由“南北高，中间低”向“南高北低”发展；3) 全局Moran’s I在0.330~0.482之间波动，表明中部六省旅游业碳排放强度具有显著的空间正相关性，旅游业碳排放强度呈现集聚特征，以高–高集聚和低–低集聚为主。对中部六省市域旅游业进行时空分析，总结变化规律，有利于因地制宜地制定旅游业碳减排政策。

Abstract: With the development of the tourism industry, its carbon emissions are increasing, posing a threat to the global climate ecology. This article selects six central provinces of China as the research object and adopts a “top-down” method to calculate the carbon emissions of the tourism industry in each city (state) of the region from 2014 to 2021. Slope trend analysis and exploratory spatial data analysis methods are used to analyze the growth trend and spatial pattern characteristics of carbon emissions in the tourism industry in each city (state) of the six central provinces. The results indicate that: 1) The tourism carbon emissions of most cities (states) in the six central provinces showed an increasing trend from 2014 to 2019. Due to the impact of the epidemic, the carbon emissions from the tourism industry in most cities (prefectures) showed a decreasing trend from 2019 to 2021; 2) The carbon emissions from the tourism industry in the six central provinces are mainly medium carbon, higher carbon, and high carbon, with spatial changes shifting from “high in the north and south, low in the middle” to “high in the south and low in the north”; 3) The global Moran’s I fluctuates between 0.330 and 0.482, indicating a significant spatial positive correlation between the carbon emission intensity of the tourism industry in the six central provinces. The carbon emission intensity of the tourism industry exhibits clustering characteristics, mainly consisting of high-high clustering and low-low clustering. Conducting a spatiotemporal analysis of the tourism industry in the six central provinces and cities and summarizing the patterns of change is beneficial for formulating carbon reduction policies for the tourism industry according to local conditions.