Spatiotemporal Variation in Carbon Emissions in China’s Tourism Industry during the COVID-19 Pandemic and Ecological Compensation Mechanism

Abstract

The COVID-19 pandemic has significantly impacted the tourism industry while providing a unique opportunity for ecological restoration in tourist attractions. This study highlights the variations in carbon emissions and the corresponding ecological compensation in China’s tourism industry across 31 provinces before and after the COVID-19 outbreak in 2019–2020. The findings reveal a substantial decline in carbon emissions stemming from China’s tourism industry in 2020, reducing by 207.0461 million tons, a remarkable 74.71% decrease compared to 2019. Shanxi exhibited the most significant reduction among the provinces, whereas Shanghai had the most minor decrease. Additionally, natural scenic areas in China experienced a 3.4% growth in carbon sinks, with an increase of 76.6271 million tons in 2020. Henan, Hubei, and Guangxi were the provinces with the highest increments. However, some provinces witnessed a decline in carbon sinks due to climate change, with Zhejiang Province, Inner Mongolia Autonomous Region, and Jilin Province displaying the most substantial reductions in 2020 compared to 2019. Furthermore, the estimated ecological compensation for the tourism industry in all 31 provinces of China amounts to approximately CNY 6.948 billion. This study provides valuable insights into carbon emission reduction in the tourism industry, ecological compensation mechanisms during unexpected public events, and the sustainable development of nature-based tourist destinations. To advance the goals of achieving peak carbon emissions and carbon neutrality, future research should prioritize tracking and classifying tourism-related carbon emissions, precisely classifying carbon sinks in natural scenic areas, and establishing interprovincial ecological compensation mechanisms.

1. Introduction

The energy consumption, pollution, and carbon emissions generated from tourism activities have gradually become significant driving factors for environmental degradation and global climate variation [1]. The transportation mode, accommodation choices, and activities undertaken by tourists during travel are the primary sources of energy consumption and carbon emissions in the tourism industry [2,3,4]. In 2008, an integrated literature research using a bottom-up approach was conducted focusing on carbon emissions in China’s tourism industry [5,6]. The study revealed that tourism transportation carbon emissions accounted for 90% of China’s total tourism-related carbon emissions from 2000 to 2013 [7]. Using carbon emission factors from UNWTO-UNEP-WMO, the carbon emissions from tourism transportation in China from 1980 to 2009 were calculated. The findings indicated that, in 2009, the amount of carbon emission from railway, road, waterway, and civil aviation tourism transportation was 6.722 million tons, 24.8 million tons, 0.078 million tons, and 29.92 million tons, respectively, with civil aviation having the highest amount of carbon emission [8]. The input–output method for tourism accommodation has been used to measure the energy consumption of tourism accommodation and explore low-carbon management paths for China’s hotel industry [9]. The outbreak and spread of COVID-19 have significantly impacted global carbon emissions [10]. In the Singapore Strait, carbon emissions from ferries, cargo ships, passenger ships, and roll-on/roll-off (Ro-Ro) vessels in 2020 decreased by 75.82%, 0.84%, 28.35%, and 0.73%, respectively, compared to 2019 [11]. France experienced a 6.6% year-on-year reduction in total carbon emissions during a 55-day lockdown in 2020 [12]. In Los Angeles, during the lockdown period in March and April 2020, carbon emissions decreased by 0.57 MtC, 0.30 MtC, 1.09 MtC, and 0.21 MtC compared to the same period in 2018 and 2019 [13]. Colombia witnessed a 28% decrease in carbon emissions during the travel lockdown in the first half of 2020 compared to the same period in 2018 [14].

Regarding vegetation carbon sink estimation, using net primary productivity (NPP) constitutes a mature approach and has become an essential indicator in global vegetation ecosystem assessments [15]. Spatiotemporal changes primarily depend on the complex interactions among vegetation, soil, and climate [16], with precipitation being the main limiting factor for the net primary productivity of vegetation in China [17]. Scholars have estimated the total carbon stock of grassland ecosystems in the Three Rivers Source Region to be 53.38 × 10⁸ tons, with an average carbon density of 14.94 kg/m², using MODIS GPP/NPP, MODIS NDVI, and various climatic data analyses [18]. The central part of Hainan Island is characterized by high carbon density in natural rubber forests, with an average carbon density ranging from 25 to 33 t/hm². In comparison, the northern part is a low-value area with an average carbon density below 20 t/hm² [19]. The calculation of net primary productivity (NPP) involves two main methods: NPP inversion models [20,21,22] and direct utilization of MODIS NPP data for measuring carbon sinks [18]. Additionally, utilizing carbon as ecological compensation is an effective strategy for facilitating regional coordination and achieving carbon neutrality [23]. Ecological compensation is called payment for ecological/environmental services (PES). It involves the restoration of ecologically damaged sites or the replacement of original ecological sites or qualities with new ones, aiming to restore the affected environmental functions and values that persist even after minimizing the impacts of intervention (mitigation) [24,25]. In China, ecological compensation includes payment for ecological/environmental services (PES) and compensates units and individuals for the costs incurred in protecting the umbrella ecological environment and functions [26]. Recent research on ecological compensation has predominantly focused on carbon compensation zoning [27,28,29,30], agriculture [31,32], and compensation willingness [33]. However, there remains a research gap in understanding the impact of unforeseen public events on carbon emissions in the tourism industry and how to conduct ecological compensation in such cases.

We aim to understand the spatial and temporal changes in carbon emissions in China’s tourism industry before and after the COVID-19 lockdown and their influencing factors, uncover the spatial variations in carbon sinks in China’s natural scenic areas, and simulate and calculate the ecological compensation amount for tourist attractions under natural recovery conditions. Building upon previous research, this study takes the reduction in carbon emissions caused by the passive shutdown of the tourism industry during the COVID-19 pandemic as a starting point. It estimates the carbon emissions of the tourism industry and the increase in vegetation carbon sinks in 31 provinces (autonomous regions and municipalities) of China from 2019 to 2020. The study adjusts the carbon compensation coefficients [34] using principal component analysis to account for different socioeconomic development conditions. It estimates the ecological compensation amount in China’s tourism industry under the impact of the COVID-19 pandemic, explores the environmental compensation mechanism for tourism destinations affected by the pandemic, and provides decision-making references for energy conservation, emission reduction, low-carbon development, and sustainable development of nature-based tourist destinations in the tourism industry.

2. Methods and Data Sources

2.1. Methods for Measuring Carbon Emissions in the Tourism Industry

Currently, there are two main methods for estimating carbon emissions in the tourism industry: the “top-down” approach and the “bottom-up” approach. The “top-down” approach involves using a comprehensive monitoring system to calculate the proportion of energy consumption and carbon emissions in the tourism industry. The “bottom-up” measurement method primarily involves calculating carbon emissions starting from the consumer end. It sequentially accounts for the carbon emissions generated during various aspects of the tourist’s journey, including transportation modes used, tourist activities undertaken, and accommodations utilized. Since China does not yet have a complete statistical monitoring system for greenhouse gas emissions, this study adopts the “bottom-up” approach to estimate carbon emissions in the tourism industry from 2019 to 2020. The estimation primarily focuses on three aspects: tourism transportation, tourism accommodation, and tourism activities.

The formula for calculating carbon emissions in the tourism industry is as follows:

$$C_n^e=C_t^e+C_a^e+C_p^e$$

(1)

$C_n^e$ is the total carbon emissions from tourism, $C_t^e$ is the carbon emissions generated by tourists during transportation, $C_a^e$ refers to the carbon emissions generated by tourist activities during accommodation, and $C_p^e$ refers to the carbon emissions generated by tourists engaging in different tourism activities.

The formula for calculating carbon emissions from tourism transportation is as follows:

$$C_t^e={\sum }_{i=1}^{31}{\sum }_{j=1}^4{Q}_{ij}^t\times P_j\times \alpha$$

(2)

In this equation, $Q_{ij}^t$ represents the passenger turnover of tourism transportation mode j (railway, road, waterway, civil aviation) in province i in year t. P_j represents the proportion of tourists using transportation mode j (

Table 1. Proportion of tourist arrivals for different tourism purposes.

PUOT	Sightsee	Lei Vac	BT	VRF	Others
POT (%)	26.35	18.55	14.80	33.50	6.80

Note: The proportion of tourists for different tourism purposes is obtained from the “China Domestic Tourism Development Annual Report 2021” published by the China Tourism Academy.

), and α represents the carbon emission factor for transportation mode j (

Table 2. Calculation parameters of carbon emissions of tourism transportation modes.

	RLY	HWY	WTWY	CV Avn
CAEF (gco2/pkm)	27	133	106	137
POT (%)	31.6	13.8	10.6	64.7

Note: The carbon emission factors and tourist proportions for different tourism transportation modes are sourced from [8]. The passenger turnover of various transportation modes in each province in China from 2019 to 2020 is obtained from the Statistical Yearbook of each province for 2020–2021 and the Ministry of Transport of the People’s Republic of China. CAEF (Purpose of the tour); POT (Proportion of tourists); RLY (Railway); HWY (Highway); WTWY (waterway); CV AVn (Civil aviation).

).

The formula for calculating carbon emissions from tourist accommodation is as follows:

$$C_a^e={\sum }_{i=1}^{31}{L}_i^E\times O_i^E\times n\times \gamma$$

(3)

In this equation, $L_i^E$ represents the total number of beds in star-rated hotels in province i in year E, $O_i^E$ represents the room occupancy rate in province i in year E, n represents 365 days, and $\gamma$ represents the carbon emission factor for tourist accommodation. According to existing literature studies [11,12,13,14,15,16], the carbon emission factor for tourist accommodation is determined as 2.458 g/bed/night. The total number of beds and room occupancy rates for star-rated hotels in each province are obtained from the EPS database.

The formula for calculating carbon emissions from tourist destinations is as follows:

$$C_p^e={\sum }_{i=1}^{31}{\sum }_{ik}^5{T}_{ih}^E\times {\beta }_{ih}$$

(4)

In this equation, $T_{ih}^E$ represents the total carbon emissions from category h tourist destinations in province i in year E, and ${\beta }_{ih}$ represents the carbon emission factor for category h tourist destinations in province i (

Table 3. Calculation parameters of carbon emissions for different tourism purposes.

PUOT	Sightsee	Lei Vac	BT	VRF	Others
CAEF (g/per)	417	1670	591	786	172

Note: The calculation parameters for carbon emissions of different tourism purposes are derived from existing literature studies [11,12,13,14,15,16], and the number of domestic and international tourists in each province is obtained from the Statistical Yearbooks of respective provinces for the years 2020–2021. PUOT (Purpose of the tour); Sightsee (Sightseeing); Leis Vac (leisure vacation); BT (Business travel); VRF (visiting relatives and friends).

). The categories of tourist destinations include sightseeing, leisure vacation, business travel, visiting relatives and friends, and others.

2.2. Methods for Estimating Carbon Sinks in Scenic Areas

Currently, there are two main approaches for estimating terrestrial ecosystem carbon sinks: “bottom-up” and “top-down” [35]. The “bottom-up” approach integrates local observations and modeling results to the entire study area, including inventory-based, eddy covariance, and ecosystem process modeling methods. On the other hand, the “top-down” approach relies on the inversion of atmospheric carbon dioxide concentrations to estimate carbon sinks.

The “bottom-up” approach allows for estimating regional carbon sinks based on more accurate observational data. However, it requires integrating multiple data types and often necessitates long-term observations, making data acquisition challenging and limiting its applicability at large scales. On the other hand, the “top-down” approach enables the calculation of vegetation carbon sinks over large areas by utilizing real-time data. Remote sensing techniques can help overcome the challenges associated with data acquisition. Therefore, in this study, MOD17A3 NPP remote sensing data are used to estimate vegetation’s net primary productivity (NPP) in national scenic areas. Additionally, annual precipitation and temperature data estimate soil microbial respiration. By combining NPP and soil microbial respiration, the net ecosystem productivity (NEP) is calculated to represent the vegetation carbon sinks in scenic areas.

2.2.1. Data Sources

NPP data are sourced from the remote sensing data product provided by the National Aeronautics and Space Administration (NASA) of the United States. The version used is MOD17A3HGF061, with a pixel resolution of 500 m and an annual temporal resolution. Temperature and precipitation data are obtained from the National Earth System Science Data Center (http://www.geodata.cn), with a resolution of 1000 m and a monthly temporal resolution. Additionally, the national scenic area boundaries are delineated based on the scenic area directories provided by the tourism departments of the 31 provinces (autonomous regions, municipalities), and the POI (Point of Interest) data for scenic spots are sourced from the Amap (AutoNavi) mapping service.

2.2.2. Calculation Method

Estimation of national scenic area vegetation carbon sinks needs to be limited to the scope of natural scenic areas. Therefore, this study first uses ArcGIS 10.2 to conduct kernel density analysis on the distribution of natural scenic areas nationwide. Based on the density classification, the distribution of scenic areas is divided into “clustered areas” and “non-clustered areas,” and the scope of the “clustered areas” is extracted. Kernel density analysis calculates the density distribution of point elements within a specified radius from each grid center, resulting in a density distribution map of features within the region.

The specific formula is as follows:

$$f\left(x\right)=\frac{1}{n{h}^2}{\sum }_{i=1}^{n}K\left(\frac{x-x_i}{h}\right)$$

(5)

In this equation, f(x) represents the kernel density equation, where h is the radius of the search window; n is the number of points falling within the threshold range, and K is the weight function. When the bandwidth is determined, the closer the distance to the core, the higher the weight value.

The vegetation carbon sink value in national scenic areas is calculated as the difference between net primary productivity (NPP) and soil microbial respiration (R_H). Considering that MODIS NPP data are already calculated based on the light use efficiency model, this study directly uses MODIS NPP data without further calculation. By subtracting NPP data from R_H, the net ecosystem productivity (NEP) is obtained as an indicator of vegetation carbon sinks.

The formula for calculating the vegetation carbon sink value is as follows:

$$NEP=NPP-R_H$$

(6)

In this equation, R_H represents soil microbial respiration, which is related to the types and quantities of microorganisms in the soil, as well as plant root exudates [36]. External climate conditions also have an impact on soil microbial respiration. This study adopts the research findings of Pei [37] and others, who established a regression model between temperature, precipitation, and carbon emissions to estimate the distribution of soil microbial respiration.

The formula for calculating soil microbial respiration is as follows:

$$R_H=0.22\times \left(Exp\right(0.0913T)+Ln(0.314R+1)\times 30\times 46.5\%)$$

(7)

where T represents temperature (°C) and R represents precipitation (mm).

2.3. Ecological Compensation Calculation Method

From the perspective of ecological environmental protection, reducing tourism development and tourism activities can also reduce carbon emissions in the tourism industry [38]. The direct impact of human activities on the natural environment decreases, and the self-recovery rate of ecosystems increases, especially the enhancement of carbon sink function in natural scenic areas [39,40,41,42]. Therefore, it is necessary to reevaluate the ecological value of tourism in scenic areas affected by the COVID-19 pandemic and provide accurate and effective ecological compensation amounts.

The difference between the estimated carbon emissions in 2019 and 2020 as well as the difference in carbon sinks in tourism-related scenic areas are used as the basis for calculating the ecological compensation in the tourism industry. This calculation determines the carbon contribution that can be used for ecological compensation. The ecological compensation amount in the tourism industry is adjusted based on the coefficient of the extent of damage caused by the COVID-19 pandemic (R, see Section 3.3.2). By combining real-time carbon prices during the COVID-19 pandemic, the ecological compensation amount for the tourism industry in each province is estimated. Calculation of effective ecological compensation amounts is as follows:

$$C^A=C_n^E+{NEP}_n^a$$

(8)

where $C_n^E=C_{n-1}^e-C_n^e$, ${NEP}_n^a={NEP}_{n-1}-{NEP}_n$, n is the year.

Calculation of ecological compensation amounts is as follows:

$$P_{pete}=P_t\times C^A\times R$$

(9)

The calculation of the ecological compensation amount involves several variables. P_pete represents the ecological compensation amount for the tourism industry affected by the COVID-19 pandemic. P_t denotes the real-time carbon price during COVID-19, which is 43.25 [36]. C^A represents the adequate carbon quantity eligible for compensation in the tourism industry, and R is the coefficient indicating the extent of damage caused by the COVID-19 pandemic on the tourism industry.

3. Results and Discussion

3.1. Analysis of Temporal and Spatial Variation and Correlation of Tourism Carbon Emissions

3.1.1. Temporal and Spatial Variation Analysis of Tourism Carbon Emissions

By estimating the carbon emissions of the tourism industry in 31 provinces of China from 2019 to 2020 (Figure 1), it was observed that the tourism industry’s carbon emissions decreased by 207.04 million tons in 2020, a reduction of 74.71% due to the impact of the COVID-19 pandemic. In terms of transportation, accommodation, and destinations, carbon emissions in 2020 decreased by 46.03%, 35.18%, and 94.95%, respectively, compared to 2019. Among them, the carbon emissions from tourism destinations experienced the most significant decline. Reduced desire for travel and limited travel range and duration resulted from the impact of the COVID-19 pandemic.

In terms of the total carbon emissions of each province and municipality, in 2020, Beijing, Shanghai, Guangdong, and Sichuan had tourism carbon emissions exceeding 5 million tons. Among them, Guangdong had the highest tourism carbon emissions in 2020, reaching 11.53 million tons. The considerable population mobility and abundant tourist attractions in Beijing, Shanghai, and Guangzhou placed them at the forefront of tourism carbon emissions during the pandemic. Heilongjiang, Jiangsu, Hunan, Hainan, Chongqing, and Fujian, six provinces and municipalities, had tourism carbon emissions exceeding 2 million tons in 2020. These provinces have relatively developed tourism industries, especially Hainan and Chongqing, which are renowned tourist destinations. Compared to more developed provinces, they experienced a milder impact from the COVID-19 pandemic, and their tourism industries were relatively well established compared to cities with lighter outbreaks.

The tourism carbon emissions of the remaining provinces in 2020 were all below 2 million tons. Due to variations in the severity of the pandemic, differences in tourism industry development, geographic location, economic development level, and epidemic prevention and control policies were observed. Among them, Shanxi was the most affected by the pandemic, with the most significant reduction in tourism carbon emissions. Shanghai was the least affected, with the smallest decrease in tourism carbon emissions.

3.1.2. Correlation Analysis of Tourism Carbon Emissions

To further explore the impact of the COVID-19 pandemic on tourism carbon emissions, Pearson correlation analysis was conducted using IBM SPSS Statistics 26 on carbon emissions data during the two years under investigation (

Table 4. Analysis of various indicators of carbon emissions in tourism from 2019 to 2020.

		Touce in 2019	Touce in 2020			Touce in 2019	Touce in 2020
Sightsee	PCCs	0.464 **	−0.03	Toua	PCCs	0.768 **	0.502 **
	Sig.	0.01	0.86		Sig.	0.00	0.00
Lei Vac	PCCs	0.464 **	−0.03	RLY	PCCs	0.393 *	0.15
	Sig.	0.01	0.86		Sig.	0.03	0.41
BT	PCCs	0.464 **	−0.03	HWY	PCCs	0.658 **	0.432 *
	Sig.	0.01	0.86		Sig.	0.00	0.02
VRF	PCCs	0.464 **	−0.03	WTWY	PCCs	0.491 **	0.361 *
	Sig.	0.01	0.86		Sig.	0.01	0.05
Others	PCCs	0.464 **	−0.03	CV Avn	PCCs	0.783 **	0.982 **
	Sig.	0.01	0.86		Sig.	0.00	0.00

Note: Correlation levels of 0.8–1.0 indicate a robust correlation, 0.6–0.8 indicate a strong correlation, 0.4–0.6 indicate a moderate correlation, 0.2–0.4 indicate a weak correlation, and 0.0–0.2 indicate a very weak or no correlation. In the Pearson correlation coefficient analysis using IBM SPSS Statistics 26, ** indicates significance at the 0.01 level (two-tailed), indicating a significant correlation, and * indicates significance at the 0.05 level (two-tailed), also indicating a significant correlation.

). In 2019, there was a significant correlation (0.464) between tourism purposes (sightseeing, leisure vacation, business travel, visiting relatives and friends, and others) and total tourism carbon emissions. However, the analysis results for 2020 showed a weak and insignificant correlation (−0.03) between tourism purposes and total tourism carbon emissions. This indicates that the occurrence of the COVID-19 pandemic has had a significant impact on people’s willingness to travel.

In 2019, there was a highly significant correlation (0.768) between tourism accommodation and total tourism carbon emissions. In 2020, the correlation remained significant but decreased to 0.502. Compared to tourism purposes, accommodation, as an essential activity in travel, showed a relatively minor decrease in correlation. However, its impact on total tourism carbon emissions remained significant.

In 2019, all modes of tourism transportation (railway, road, water transport, civil aviation) showed significant correlations. Civil aviation had the highest correlation (0.783), followed by road (0.685), water transport (0.491), and railway (0.393), with decreasing levels of correlation. This indicates that before the occurrence of the pandemic, tourism transportation in China was highly active and characterized by diverse travel modes. In 2020, due to the impact of the COVID-19 pandemic, the number of people choosing railway travel decreased. The correlation between railway travel carbon emissions and total tourism carbon emissions decreased and became insignificant (0.15). However, civil aviation (0.982), water transport (0.361), and road (0.432) transportation remained significant. The increased correlation between civil aviation carbon emissions and tourism carbon emissions suggests that during unexpected public events like the COVID-19 pandemic, most people, for risk avoidance purposes, choose short-duration and higher-priced civil aviation for travel. Traditional and relatively lower-risk travel modes, water transport and the road, still showed significant correlations.

3.2. Spatial Analysis of Carbon Sinks in Natural Scenic Areas

Using the point-of-interest (POI) data of national A-level-and-above tourist attractions, ArcGIS 10.2 was employed to conduct kernel density analysis of the attractions. Density clusters were extracted to determine the spatial scope for subsequent carbon sink calculations (Figure 2). A-level-and-above scenic areas in China generally exhibit an agglomerative distribution, with a spatial pattern characterized by more in the southeast, less in the northwest. These scenic areas are mainly concentrated in central China’s eastern coastal economic belt and economically developed regions. In terms of quantity, the number of A-level-and-above scenic areas in eastern China far exceeds that in the western region, and they are primarily distributed in Beijing, Shanghai, Shandong, the Yangtze River Delta, and the Pearl River Delta, which are areas with rich historical and cultural heritage. The northeastern region has a relatively larger number of scenic areas, with small-scale high-density clusters observed in Liaoning Province.

In contrast, other areas have fewer scenic areas and moderate clustering. The southern, central, and southwestern provinces generally have a relatively even distribution of scenic areas, with a moderate to moderate-to-general degree of clustering. However, Guangdong, Shaanxi, Henan, Chongqing, and Sichuan have higher-density cluster areas related to regional economic development and historical and cultural factors. Xinjiang, Tibet, and Qinghai have a small number of scenic areas and exhibit large low-density clusters. Based on the spatial analysis of tourist attractions, this study categorizes highly concentrated, moderately concentrated, moderately concentrated, and generally concentrated areas as “cluster areas.” It calculates the vegetation carbon sinks within these areas to represent the overall vegetation carbon sinks of scenic areas nationwide. Areas with low clustering are classified as “non-cluster areas” and are not included in the carbon sink calculation.

The overall distribution pattern of vegetation carbon sinks in scenic areas across provinces in China in 2019–2020 exhibits similar regional variations (Figure 3 and Figure 4). The spatial distribution of vegetation carbon sinks in scenic areas is closely related to climate, showing a “more in the southeast, less in the northwest” pattern. Regarding total carbon sink quantity, southern provinces have a higher number of carbon sinks in their scenic areas. The warm and humid climate in southern regions is conducive to vegetation growth, and the scenic areas are larger and more concentrated in distribution. Northwestern regions such as Tibet, Xinjiang, Qinghai, and Ningxia have higher elevations and dry and cold climates, unfavorable for vegetation growth. The scenic areas in these regions are smaller, less in distribution, and have a lower carbon sink quantity. Although Beijing, Tianjin, and Shanghai are located in the eastern coastal region with a favorable vegetation growth climate, most of their economically developed scenic areas are focused on shopping and consumption, with smaller ecological areas and vegetation coverage, resulting in a lower carbon sink quantity.

In 2020, the vegetation carbon sink quantity in scenic areas increased by 76.6271 million tons compared to 2019. Due to reduced human activities, most provinces witnessed an increase in vegetation carbon sinks in scenic areas in 2020 compared to 2019. Regions with positive differences in vegetation carbon sinks between the two years are mainly concentrated in the central and southern regions, where natural scenic areas are prominent and heavily impacted by the COVID-19 pandemic. Among them, Henan Province, Hubei Province, and Guangxi Zhuang Autonomous Region showed the most significant increase in carbon sink quantity in their natural scenic areas. In the northwestern region, where human activities were reduced, there was also an increasing trend in the carbon sink quantity of scenic areas. However, the magnitude of the increase was relatively small. In contrast, the northern and southwestern regions experienced a negative growth in carbon sinks due to temperature, precipitation, and the carbon sink quantity required for economic development. Among them, Zhejiang Province, Inner Mongolia Autonomous Region, and Jilin Province witnessed the largest decrease in carbon sinks.

3.3. Ecological Compensation Mechanism for the Tourism Industry during the COVID-19 Pandemic

3.3.1. Calculation of Effective Ecological Compensation in the Tourism Industry

In 2019 and 2020, the carbon emissions of the tourism industry were 277.12 and 70.08 million tons, respectively, indicating a decrease of 20,704.61 million tons. This represents a reduction of 74.71% compared to the previous year. The carbon sequestration of the scenic areas in 2019 and 2020 was 2247.67 and 2324.30 million tons, respectively, showing an increase of 766.27 million tons, corresponding to a growth of 3.4% compared to the previous year. From the perspective of carbon balance, adequate ecological compensation in the tourism industry during 2019–2020 includes a reduction in carbon emissions in the tourism sector and an increase in carbon sequestration in scenic areas. Therefore, the total adequate ecological compensation in the tourism industry during this period amounted to 28,367.32 million tons.

In most provinces, the total amount of effective ecological compensation during 2019–2020 was positive (Figure 5). This indicates that despite the prevalence of the COVID-19 pandemic, which led to the suspension of the tourism industry in China, when not considering the differences in climate conditions between 2019 and 2020, the shutdown of the tourism industry reduced carbon emissions. Simultaneously, the decrease in human activities temporarily improved vegetation growth conditions in tourist areas, enhancing their carbon sequestration capacity. Regarding the comprehensive carbon emissions and carbon sink volume, the tourism industry possesses considerable ecological compensation in their carbon budget.

From a spatial distribution perspective, regions with more significant effective ecological compensation are primarily located in the central and southern parts of the country. Among them, Henan, Guangdong, and Hubei have compensation amounts reaching 20,000 tons. The compensation amounts in the northwest region are generally lower, as negative compensation occurs in the northern and northeastern regions due to a significant decrease in overall vegetation carbon sequestration.

3.3.2. Correction of Ecological Compensation Coefficient in the Tourism Industry

We aimed to accurately measure the ecological benefits increased by the impact of the COVID-19 pandemic on scenic areas. IBM SPSS Statistics 26 software was used to analyze the relevant indicators that affected the tourism industry during the COVID-19 pandemic, excluding carbon emissions from tourism. The carbon compensation coefficient was adjusted under different economic and social development conditions to obtain the damage degree coefficient (R) of the tourism industry affected by the COVID-19 pandemic.

According to

Table 5. Correlation matrix of indicators.

	PECG	AMOR	REEC	LONS	AROT	PRON	GDOE	NUOC
PECG	1.000	0.179	−0.429	−0.163	0.515	0.731	0.448	0.037
AMOR	−0.179	1.000	−0.002	−0.457	−0.312	0.297	−0.685	−0.049
REEC	−0.429	−0.002	1.000	−0.034	0.151	−0.427	−0.217	−0.178
LONS	−0.163	−0.457	−0.034	1.000	0.662	0.346	0.492	0.221
AROT	0.515	−0.312	0.151	0.662	1.000	−0.279	0.189	0.131
PRON	0.731	−0.297	−0.427	0.346	−0.279	1.000	0.473	0.075
GDOE	0.448	−0.685	−0.217	0.492	0.189	0.473	1.000	0.104
NUOC	0.037	0.049	−0.178	0.221	0.131	0.075	0.104	1.000

Note: Correlation levels of 0.8–1.0 indicate a robust correlation, 0.6–0.8 indicate a strong correlation, 0.4–0.6 indicate a moderate correlation, 0.2–0.4 indicate a weak correlation, and 0.0–0.2 indicate a very weak or no correlation. PECG (Per capita GDP by region); AMOR (Amount of reduced tourism revenue); REEC (regional Engel coefficient); LONS (area of local nature scenic spots); AROT (Area of tourism scenic spots in each province); PRON (The proportion of the area covered by natural scenic areas); GDOE (GDP of each province); NUOC (Number of confirmed COVID-19 cases.). Correlation analysis using 2020 data.

, it can be observed that there are significant correlations between the following indicators: PECG and AROT (0.515), PRON (0.731); AMOR and GDOE (−0.685); and LONS and AROT (0.662). These variables exhibit strong direct correlations, indicating high information overlap. The correlation between PECG and PRON is 0.731, suggesting that the level of regional tourism development or the high concentration of natural scenic areas strongly impacts the per capita GDP in that region. The same logic applies to the correlation between PECG and AROT. Therefore, when selecting indicators for ecological compensation in the tourism industry, it is advisable to consider including PRON. The correlation between AMOR and GDOE is significant (−0.685), indicating that the decrease in tourism revenue significantly impacted the local area’s total GDP. Therefore, to calculate the ecological compensation that aligns more closely with the economic losses suffered by the regional tourism industry, selecting AMOR as a research indicator is advisable when studying ecological compensation in the tourism industry. The correlation between LONS and AROT is significant (0.662), indicating that the size of natural scenic areas explicitly influences the size of regional tourist attractions. Moreover, natural scenic areas have a more vital carbon sequestration capacity than general tourist attractions. Therefore, the scope of natural scenic areas should be addressed in studying ecological compensation in the tourism industry.

The correlation between AMOR and LONS is −0.457, while the correlations between REEC and PECG (−0.429), REEC and PRON (−0.427), GDOE and PECG (0.448), GDOE and LONS (0.492), and GDOE and PRON (0.473) are all above 0.4. These indicators have relatively strong correlations compared to others. The correlation between PECG and GDOE (0.448) is particularly significant among them. From the perspective of overall regional development, we select GDOE as the more appropriate indicator. REEC and PRON (−0.426) exhibit a negative correlation, indicating that the higher the regional Engel coefficient, the smaller the area of natural scenic spots in that region. This reflects the willingness of the region to compensate ecologically for natural scenic areas. Therefore, REEC is chosen for the corresponding analysis considering the coefficient adjustment.

According to

Table 6. Explanation of Total Variance of Indicators.

Initial Eigenvalues				Extract the Sum of Squares of the Loads
Components	Total	Peov (%)	Cum (%)	Total	Peov (%)	Cum (%)
1	2.809	35.109	35.109	2.809	35.109	35.109
2	2.26	28.249	63.358	2.26	28.249	63.358
3	1.156	14.446	77.804	1.156	14.446	77.804
4	0.683	8.538	86.342
5	0.557	6.962	93.304
6	0.282	3.524	96.828
7	0.206	2.575	99.403
8	0.048	0.597	100

, the first three eigenvalues are more significant than 1, but their cumulative contribution value is relatively small at 77.804%. Therefore, it is more reasonable to select the first four components with a cumulative contribution value greater than 85% in analyzing the indicator component matrix (

Table 7. Explanation of Total Variance of Indicators.

	Components
	1	2	3	4
PECG	0.647	−0.674	−0.082	0.144
AMOR	−0.665	−0.4	0.416	−0.152
REEC	−0.469	0.391	−0.445	0.563
LONS	0.555	0.698	0.157	−0.153
AROT	0.073	0.909	0.128	−0.205
PRON	0.807	−0.359	0.017	−0.061
GDOE	0.85	0.194	−0.182	0.146
NUOC	0.197	0.121	0.838	0.481

). The component matrix is shown in Table 7. In the first component, PRON (0.807) and GDOE (0.85) have a relatively large proportion, indicating that the proportion of natural scenic areas in A-level scenic tourist areas and the GDP of each province have a significant impact on the study of ecological compensation in tourist areas. Therefore, PRON and GDOE can be selected as variable indicators for the R coefficient. The second component primarily reflects PECG (−0.674), LONS (0.698), and AROT (0.909). However, since PRON has already replaced AROT in the first component, it is excluded and not considered. Therefore, in the second component, PECG and LONS are variable indicators for ecological compensation in the tourism industry. The third and fourth components primarily reflect REEC and NUOC, with contributions of −0.445 and 0.838, respectively. In the fourth component, REEC and NUOC have significant contributions of 0.563 and 0.481, respectively. In the third and fourth components, REEC in 2020 represents the residents’ living standards more than PECG in the second component. It is more reasonable to consider REEC instead of PECG as the variable indicator for the R coefficient when conducting ecological compensation in the region.

In conclusion, the relevant indicators for the R coefficient are determined as follows: 2020 reduction in tourism revenue (AMOR), 2020 regional Engel coefficient (REEC), 2020 ratio of natural scenic area to total tourist area (PRON), and 2020 GDP of each province (GDOE). Subsequently, these four indicators are used to derive Formula (10).

In Formula (10), S represents the GDP of each province (city), and Sn represents the total reduction in tourism revenue in the study area due to the COVID-19 pandemic. The ratio of these two variables visually represents the impact on the regional tourism economy caused by the pandemic. A logarithmic function is used as a control mechanism to regulate the overall ratio within a reasonable range.

t represents the Engel coefficient of each province (city). The Engel coefficient can effectively reflect the living standards of the regional population and the willingness to pay for ecological values under different levels of economic development and living standards. Using the natural logarithm as the base, when the Engel coefficient is significant, it indicates a lower willingness to pay for ecological values in that area. As a result, the R-value becomes smaller, indicating less ecological compensation for that region. This reflects the consideration of people’s willingness to pay in the calculation of R. Q represents the natural aggregation degree of scenic areas in each province (city), which is equal to the proportion of the natural scenic areas in each province (city) to the total area of scenic areas. It is listed to regulate the overall R-value, making it more in line with the scope of ecological compensation in the tourism industry.

The selection of R design indicators considers the differences in economic development levels and the density of natural scenic areas in different regions. By incorporating the R coefficient into the calculation of ecological compensation amount based on adequate ecological compensation and carbon price, it better reflects the actual damage to scenic tourism areas in different regions.

$$R=Q\times \frac{e^t\times \ln S_n}{(1+e^t)\times \ln S}$$

(10)

3.3.3. Calculation of Ecological Compensation Amounts in Tourism Industry

Based on the estimation of carbon emissions and vegetation carbon sequestration in the tourism industry in China from 2019 to 2020 and considering the average carbon trading price in the Chinese carbon trading market since its establishment until February 2022, the carbon ecological compensation amount for scenic areas in the 31 provinces of China in 2020 is calculated (Figure 6).

The four provinces of Inner Mongolia, Jilin, Qinghai, and Ningxia have relatively lower reductions in carbon emissions and carbon sequestration in the tourism industry. The reduction in carbon emissions in the tourism industry in Inner Mongolia, Qinghai, and Ningxia is below 2 million tons. Among them, the reduction in carbon emissions in the tourism industry in Qinghai and Ningxia is around 500,000 tons. In 2020, the number of confirmed COVID-19 cases in Ningxia and Qinghai was less than one hundred, and the impact of the pandemic on people’s regular travel was relatively small. However, their tourism revenue experienced a significant decline of 50%. Therefore, for the effective ecological compensation amount in their tourism sector, the reduction in carbon emissions in their tourism industry in 2020 is used, and the calculation is adjusted using the coefficient representing the extent of damage caused by the COVID-19 pandemic on the tourism industry.

The provinces of Beijing, Shanghai, Jiangsu, Anhui, Shandong, Henan, Hubei, and Guangxi have ecological compensation amounts in the tourism industry exceeding CNY 400 million. Among these, except for Beijing and Shanghai, the other six provinces experienced an 80% reduction in carbon emissions in the tourism industry in 2020. Beijing and Shanghai had many confirmed COVID-19 cases in 2020 and were severely affected by the pandemic. They also had more natural scenic areas than most provinces and cities. In terms of incremental carbon sink recovery, Henan and Hubei provinces rank first and second in carbon sequestration recovery among the 31 provinces and municipalities. Guangxi has a carbon sequestration recovery volume of up to 9.3 million tons. Apart from Guangxi, the other seven provinces have a developed economy and a low regional Engel coefficient, indicating a stronger willingness among residents for compensation than other provinces.

Provinces and municipalities with ecological tourism compensation exceeding CNY 200 million include Hebei, Shanxi, Jiangxi, Guangdong, Chongqing, and Guizhou. Apart from Guangdong and Chongqing, the other five provinces reduced tourism-related carbon emissions by over 80% in 2020. Hebei, Shanxi, and Guangdong have witnessed a carbon sequestration increase in their scenic areas of more than 6.5 million tons. These seven provinces have experienced a tourism revenue decline of over 50% compared to 2020. The pandemic has significantly affected Jiangxi and Guangdong, while the impact on the other provinces has been relatively minor. However, these provinces have a relatively high concentration of natural scenic areas.

Provinces and municipalities with ecological tourism compensation exceeding CNY 100 million include Tianjin, Liaoning, Zhejiang, Fujian, Hunan, Sichuan, and Shaanxi. These seven provinces and municipalities achieved an average reduction in tourism-related carbon emissions of around 75% in 2020. Except for Tianjin, Hunan, and Shaanxi, which showed positive carbon sequestration increases, the other provinces had negative carbon sequestration increments in 2020. These provinces have a lower concentration of scenic areas compared to the previous two tiers of provinces.

Provinces and regions with ecological tourism compensation below CNY 100 million include Heilongjiang, Hainan, Yunnan, Tibet, Gansu, and Xinjiang. These six provinces and regions had relatively lower reductions in tourism-related carbon emissions in 2020 compared to other provinces. They were less affected by the pandemic than other provinces. They had a relatively weaker concentration of natural scenic areas, higher resident Engel coefficients, lower compensation willingness, and lower compensation amounts. The calculation and categorization of ecological compensation for each province provide a sustainable development plan with ecological compensation as the focus for nature-oriented ecological scenic areas.

4. Conclusions and Prospects

This study takes the provincial level as the research scale to estimate total carbon emissions, carbon sink increment in tourist areas, and the ecological compensation amount of the regional tourism industry in 31 provinces in China from 2019 to 2020. Overall carbon emissions from the tourism industry in the 31 provinces showed a declining trend in 2020, with a reduction of over 50% on average. Quantities of carbon sinks in tourist areas showed an upward trend, increasing by 3.4%. The regions with negative growth were mainly concentrated in provinces where the epidemic was less severe, and the tourism industry accounted for a more significant proportion of the overall GDP. Except for Inner Mongolia, Jilin, Qinghai, and Ningxia, which had a negative adequate ecological compensation amount, the remaining provinces had a positive effective ecological compensation amount.

Based on each province’s adequate ecological compensation amount, the estimated ecological compensation amount is CNY 6.948 billion. Provinces with higher ecological compensation amounts are mainly concentrated in regions severely affected by the epidemic, with a concentration of natural scenic areas, strong regional compensation capacity, and a high willingness of the population to compensate.

The development of tourism has a promoting effect on carbon emissions [43,44]. Factors such as the number of tourists and tourism income are the main factors influencing carbon emissions in the tourism industry [45]. The impact of the COVID-19 pandemic is mainly reflected in a sharp decline in the number of tourists and tourism income [46]. From the perspective of tourism supply and demand, the COVID-19 pandemic has intensified the risks and burdens of local governments and tourism enterprises regarding development. Additionally, pandemic control measures may reduce tourists’ willingness and demand for tourism activities [38]. From the perspective of ecological environment protection, tourism activities significantly impact the ecological environment of tourism destinations. Therefore, reducing tourism development and activities will also decrease carbon emissions in the tourism industry. Some scholars have suggested that research on ecological compensation in the tourism industry should be linked to the number of tourists, tourism income, and disposable income of residents in scenic areas [47]. The results of this study validate these viewpoints.

The emergence of the COVID-19 pandemic has indeed dealt a blow to developing the tourism industry. However, it has also facilitated the overall ecological recovery of tourism destinations. It has provided a research background for assessing the recovery capacity of the tourism ecosystem with minimal human impact during the containment of the pandemic. Different tourism destinations have other ecological systems and vegetation restoration capacities. To achieve a harmonious balance between economic development and environmental conservation for different types of ecosystems (natural, artificial, semi-natural, semi-artificial), corresponding periods for ecological restoration should be arranged based on the characteristics of each destination. The design of the impact coefficient for the tourism industry affected by the COVID-19 pandemic provides a reference for developing ecological compensation schemes for the affected tourism sector.

Calculation methods for carbon emissions and the study of regional ecological compensation have been rapidly developing in China. This study primarily focuses on government-led compensation models within provincial boundaries and does not examine spillover effects, externalities, and ecological compensation between provinces in the tourism industry. In quantifying the indicators, some low-agglomeration scenic areas were excluded by referring to mature research methods, resulting in specific errors in estimating the carbon sequestration of highly agglomerated scenic areas.

To further expand and improve upon the carbon emissions of China’s tourism industry and its future ecological compensation mechanism and to promote the sustainable development of natural protected areas, it is necessary to quickly compensate for the economic losses caused by unexpected public events such as the COVID-19 pandemic. This study believes that future research on carbon emissions and ecological compensation in the tourism industry in China should focus on the following three aspects:

(1)

Establish a comprehensive carbon emissions monitoring system. Utilize atmospheric observations, satellite data, big data, and other means to monitor carbon emissions in real time. Construct a carbon factor database to track tourism carbon emissions in real time;

(2)

Conduct refined research on the classification of tourist attractions. There are various types of tourist attractions, and their carbon sequestration capacities differ. The corresponding ecological compensation levels also vary. Ecological compensation research in the tourism industry can be classified and studied according to cultural heritage sites, scenic spots, natural landscapes, and red tourism.

(3)

Promote research on interprovincial ecological compensation systems in the tourism industry. Establish and improve upon ecological compensation systems for the tourism industry, promote low-carbon development in the tourism industry, and preserve the ecological environment of natural scenic areas. Natural scenic clusters often have spillover effects, so exploring interprovincial ecological compensation systems in the tourism industry and promoting regional economic exchanges and linkages are crucial.