Impact of the Participation of the Tourism Sector on Carbon Emission Reduction in the Tourism Industry

Abstract

Carbon emissions in the tourism industry stem from independent industries (e.g., aviation, accommodation, and catering), but it is unclear whether the participation of the tourism sector promotes carbon emission reduction. In China, the tourism sector has been involved in the formulation and implementation of low-carbon tourism policies since 2017, providing a quasi-natural experimental condition for studying whether the participation of the tourism sector can promote the reduction of carbon emission in the tourism industry. Through a quantitative analysis, we find that the participation of the tourism sector promotes the carbon emission reduction. In particular, the participation of tourism departments in the formulation and implementation of low-carbon tourism policies leads to 1.622 million more tons (1% significance level) of carbon emission reduction in tourism-developed cities than in other cities. The participation of the tourism sector can promote carbon emission reduction in the transportation, construction, and commodity production sectors. It can also promote a low-carbon lifestyle. Finally, we suggest that the tourism industry should use the market to promote a carbon peak and use technology to achieve carbon neutrality. This study is of great significance for the reduction of carbon emissions in China’s tourism industry.

1. Introduction

The 26th Conference of the Parties (COP 26) to the United Nations Framework Convention on Climate Change (UNFCCC) reaffirmed the Paris Agreement’s temperature increase target “to hold the increase in global mean surface temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the increase to 1.5 °C”. If appropriate measures are not adopted to halt the growth of greenhouse gas emissions, the global temperature could rise 4–5 °C above pre-industrial levels by the end of the century [1], resulting in a sea level rise, reduced crop yields, the spread of infectious diseases, and poor drinking water resources [1]. In addition, the increased carbon dioxide (CO₂) concentration could lead to ocean acidification, which would in turn deal a devastating blow to marine ecosystems [2]. Therefore, some countries, including China, have set the goal of carbon neutrality [3].

Tourism relies heavily on carbon-intensive industries [4,5], including oil processing, steel, construction, and electricity. Both the construction of cultural or natural tourist attractions and the development of tourist transportation depend on these industries. The carbon footprint of tourism (the “carbon consumption” of an individual or a group) includes both direct emissions from tourism activities (e.g., transport, food, and accommodation) and indirect emissions hidden in goods purchased by tourists (e.g., the production and transport of goods) [6]. The global annual direct carbon emissions of tourism are 2.9 billion tons CO₂-e (the CO₂ emission equivalent) [7]. Such emissions could increase by 1.6 billion tons of CO₂-e per year and contribute 8% to global carbon emissions (note: global emissions refer to anthropogenic emissions) if the life cycle of goods associated with tourism and indirect energy requirements during travel are taken into account [7]. Greenhouse gases emitted by tourism include CO₂ and methane (CH₄). CO₂ is the main greenhouse gas emitted by tourism, accounting for 72% of total emissions (3.2 billion tons of CO₂-e, [7]). The main emission sources are transportation (planes, cars, trains), electricity (hotels and restaurants), and fossil fuel combustion (involved in the manufacturing process of the goods purchased by tourists) [8]. CH₄ is the second most abundant greenhouse gas emitted by tourism, accounting for 16% of total emissions (700 million tons of CO₂-e, [7]). The main emission sources are livestock and aquaculture at the food processing and retail supply ends [9].

The rapid growth of tourism is bound to lead to increasing carbon emissions [10]. According to a study by the United Nations World Tourism Organization in December 2019 at COP 25 to the UNFCCC, tourism’s CO₂ emissions will increase by 25% by 2030 compared to 2016 levels, according to the current emission intensity and tourism trends [11]. Tourism-related carbon emissions from the transport sector will increase to approximately 2 billion tons of CO₂-e by 2030, contributing to 22% of total global transport sector emissions and 5.3% of total global emissions [11]. COP 26 to the UNFCCC in 2021 called for a 50% reduction in tourism emissions by 2030. This is clearly not achievable without the decarbonization of the tourism industry.

The total carbon emissions from China’s tourism industry (including direct and indirect emissions) are still uncertain. This is because China has not established a statistical system of carbon emissions of the tourism industry and there are no independent data on carbon emissions in the tourism industry in the form of an input–output table [12]. The bottom-up method and top-down methods have been used to calculate the carbon emissions in China. Using these methods, China’s carbon emissions from tourism were estimated [13,14] and the effects of various sub-sectors in the tourism sector on carbon emissions were explored [15]. Scholars have mainly used bottom-up methods to estimate the tourism related emissions from transportation, accommodation, and tourism activities [16,17] and the results show an increase in carbon emissions in tourism in China, from 1468.08 × 10⁴ tons in 1990 to 11,568.17 × 10⁴ tons in 2012 [17]. The results also show a subsequent increase, from 9954.57 × 10⁴ tons in 2013 to 17,565.51 × 10⁴ tons in 2019 [12]. With the continuous development of China’s economy and the increase in residents’ incomes, more people are expected to be involved in the tourism industry. If tourism still maintains the emission characteristics of the present stage, carbon emission is bound to increase in the future. The transportation sector is the largest contributor to the carbon emissions of tourism in China, accounting for 78–85% of total emissions [12,17]. The carbon emission of civil aviation is the main emission source of the current transportation sector, accounting for approximately 80% of total transportation emissions [12].

Except for the calculation of the tourism-related carbon emissions, studies also probed the relationship between the development of tourism and carbon emissions in China. Both the CO₂ emissions and the number of tourists increased from 2014 to 2017 [8], while the CO₂ emission intensity decreased during this period [18]. Although the tourism sector contributed to the total carbon emissions, the development of tourism contributes to the decrease in carbon emission intensities [18].

The existing studies also focused on the characteristics of tourism carbon emissions, including spatial and temporal differences, socio-economic driving factors and the impact of tourism development on carbon emissions. There are significant regional differences in tourism-related carbon emissions and emission efficiency in China [8]. In particular, the emission efficiency is high in the east and low in the west. At present, most provinces and regions are still at the inefficiency level and there is a large room for improvement in the emission efficiency [19]. In terms of the driving factors, tourism scale and consumption could promote carbon emissions, while the improvement of energy intensity would reduce the carbon emissions of tourism [8]. The tourism industry agglomeration has a U-shaped (inverted U-shaped) relationship with carbon emissions in tourism-developed (under-developed) areas. Besides, a few studies identified the impact of tourism developments on China’s carbon emissions and found that the development of tourism can reduce carbon intensity [20].

In summary, previous studies mainly focused on the characteristics of tourism’s carbon emissions, including spatial and temporal differences, socio-economic driving factors, and the impact of tourism development on carbon emissions. The impact of the participation of the tourism sector on carbon emission reduction is still unclear. In reality, as the aviation, accommodation, catering, and other industries involved in the tourism sector are all independent industries, tourism enterprises or authorities lack sufficient jurisdiction. Therefore, low-carbon policies are often not targeted at the tourism industry and the tourism sector may not be involved in the management of carbon emissions [12]. Jin et al. [21] summarized policies related to low-carbon tourism in China. Before 2017, low-carbon measures for tourism mainly came from the carbon emission reduction policies of other sectors (e.g., the transportation department) and the tourism department did not participate in the formulation and implementation of low-carbon policies. Since 2017, the tourism department has been involved in the formulation and implementation of low-carbon tourism policies. For example, the “Opinions on Promoting the Integrated Development of Transport and Tourism”, jointly formulated by the tourism department and six other departments (e.g., the transportation department), issued specific measures for low-carbon tourism in great detail. In addition, the China Culture and Tourism Administration issued the “Basic Requirements and Evaluation of Tourism Homestay”, which used energy conservation and environmental protection as an important evaluation index. Such a context provides a quasi-natural experimental condition for studying whether the participation of the tourism sector can promote carbon emission reduction in the tourism industry.

The aim of this study is to clarify whether the participation of the tourism sector promotes the reduction in carbon emissions in the tourism industry and to further explain why such participation can promote carbon emission reduction. Based on the analysis, policy suggestions for the tourism industry in China are given.

2. Materials and Methods

2.1. Empirical Model

To verify whether the participation of the tourism sector promotes carbon emission reduction in the tourism industry, we selected 152 cities at and above the prefecture-level from 2014 to 2017 as the research sample. The treatment group consisted of 19 tourism-developed cities, which were the top 19 cities in terms of tourism revenue (e.g., Beijing, Shenzhen, Hangzhou, Suzhou, Nanjing, Wuhan, Zhengzhou, Xian, Chengdu, Qingdao, Ningbo, Changzhou, Wuxi, Dalian, Shaoxing, Yantai, Jiaxing, Taizhou, and Huzhou). Most of the 19 tourism-developed cities were provincial capital cities. The remaining cities were classified as the control group.

A DID framework was used to determine whether the participation of the tourism sector led to a greater carbon emission reduction in the tourism-developed cities than in other cities. The DID method has been widely used in policy evaluation [22,23,24] and can effectively avoid the endogenous and missing variables problem.

We used the standard DID estimation model as follows:

$$Carbo{n}_{it}={\beta }_0+{\beta }_1 Trea{t}_{it}\times Pos{t}_{it}+{{\displaystyle \sum }}^{}{\gamma }_X\times Contro{l}_{it}+u_i+{\tau }_t+{\epsilon }_{it}$$

(1)

where $i$ represents the city and $t$ represents the year. $Carbo{n}_{it}$ represents the emissions of CO₂ of city $i$ in year $t$. $Trea{t}_{it}$ equals 1 if a given city is a tourism-developed city and 0 otherwise. $Pos{t}_{it}$ equals 1 only after a city has carried out the low-carbon policy. The DID interaction term in this study is the independent variable, that is, $Trea{t}_{it}\times Pos{t}_{it}$. For treated cities that have implemented the low-carbon policy, the value of $Trea{t}_{it}$ is 1; otherwise, it is 0. Before the policy has been implemented (before 2017), the value of $Pos{t}_{it}$ is 0; otherwise, it is 1. The coefficient of $Trea{t}_{it}\times Pos{t}_{it}$ reflects the net effect of the low-carbon policy of the tourism sector participation on tourism-developed cities.

We focused on ${\beta }_1$ in this study, which described the net effect. To ensure that the DID model is robust, it is necessary to add the control variables affecting dependent variables, where $Contro{l}_{it}$ indicates all the control variables we select. There were a total of 10 control variables, including population, Gross Domestic Product (GDP), land used for urban construction, green covered area, tertiary industry as a percentage of GDP, secondary industry as a percentage of GDP, primary industry as a percentage of GDP, number of industrial enterprises above a designated size, electricity consumption, and total gas supply. $u_i$ represents the city fixed effect, ${\tau }_t$ represents the year fixed effect, and ${\epsilon }_{it}$ denotes the random disturbance term.

Rosenbaum and Rubin [25] used the propensity score as a distance function to match the control group with the treated group, which is called propensity score matching (PSM). This method reduces multiple matching covariates to a single index, that is, the propensity score, thus avoiding the “curse of dimensionality”. In our study, we used PSM to find an effective control group and then estimated the impact of the tourism low-carbon policy on the emissions of CO₂ more reliably through PSM-DID. Details regarding PSM-DID and the robust checks of the results are used in this study. In our study, we used the one-to-one nearest neighbor matching method to find the control group. The results show that almost all treated samples matched in the common support area, suggesting that the matched results are representative.

Figure 1 denotes the differences of covariates before and after matching in the treatment group and control group. It can be seen that all points are close to the pure line of 0 after matching, which means that the deviation between the variables has been greatly reduced. This means that PSM makes the treatment group more comparable to the control group. The results show that almost all treatment samples match in the common support area, suggesting that the matched results are representative.

Table 1. Covariate differences before and after matching (Carbon).

Variable		Mean		%Reduction		t-test
	Sample	Treated	Control	%Bias	Bias	t	p > t
pop	Unmatched	666.1	442.09	82.2		6.89	0
	Matched	664.9	727.33	−22.9	72.1	−1.26	0.21
lgdp	Unmatched	18.093	16.331	228.1		17.12	0
	Matched	17.919	18.01	−11.8	94.8	−0.72	0.476
land	Unmatched	480.53	113	143.3		18.72	0
	Matched	422.43	450.48	−10.9	92.4	−0.48	0.629
gca	Unmatched	42.904	39.585	54.4		3.78	0
	Matched	42.63	40.411	36.4	33.1	1.85	0.067
pi	Unmatched	3.1022	10.899	−165.7		−10.56	0
	Matched	3.5579	2.8912	14.2	91.4	1.68	0.096
si	Unmatched	43.963	47.605	−43.1		−3.27	0.001
	Matched	44.612	47.52	−34.4	20.2	−2.18	0.032
ti	Unmatched	52.935	41.496	126.1		9.8	0
	Matched	51.831	49.587	24.7	80.4	1.54	0.125
ie	Unmatched	4253.8	1084.4	192.4		21.53	0
	Matched	3374.4	3779.3	−24.6	87.2	−1.64	0.104
ec	Unmatched	4.20 × 106	9.30 × 105	158.1		18.83	0
	Matched	3.30 × 106	3.50 × 106	−10.8	93.2	−0.47	0.642
gas	Unmatched	1.80 × 105	22,448	68.5		10.64	0
	Matched	1.40 × 105	1.20 × 105	10	85.4	0.57	0.57

if variance ratio outside [0.63; 1.58] for U and [0.59; 1.69] for M.

shows the covariate differences before and after matching. Additionally, it indicates that the differences of covariates between the treatment group and control group are not significant after matching, which means that the regression results based on PSM are more reliable.

2.2. Data Sources and Description

The CO₂ emission data in 152 cities are from Chen et al. [26] because there are no independent data on carbon emissions in the tourism industry in the form of an input–output table. Similarly, data from the transport sector, which contributes the most carbon emissions in the tourism industry, cannot be obtained accurately. We used the CO₂ emission data of each city, instead of the transport sector data.

Control variables including population, GDP, land used for urban construction, green covered area, tertiary industry as a percentage of GDP, secondary industry as a percentage of GDP, primary industry as a percentage of GDP, the number of industrial enterprises above a designated size, electricity consumption, and total gas supply are from the China City Statistical Yearbook [27,28,29,30].

Table 2. Descriptive statistics.

Variables	Variables Description	Treatment Group			Control Group
		Mean	Min	Max	Mean	Min	Max
Carbon	CO2 emissions	60.877	25.973	129.601	27.612	4.625	150.049
POP	Population	666.102	263.1	1417	442.089	31	1258
LGDP	Natural logarithm of per capita GDP	18.093	16.200	19.451	16.331	13.904	19.039
LAND	Land used for urban construction	480.526	96	1603	112.998	14	995
GCA	Green Covered Area as % of Completed Area	42.904	32.5	61.58	39.585	2.71	95.25
PI	Primary industry as percentage to GRP	3.102	0.03	7.11	10.899	0.25	28.04
SI	Secondary industry as percentage to GRP	43.963	19.01	54.1	47.605	13.57	75.49
TI	Tertiary industry as percentage to GRP	52.935	39.91	80.56	41.496	21.9	77.29
IE	Number of industrial enterprises above designated size	4253.816	1146	10,432	1084.365	21	5525
EC	Electricity consumption	4,186,158	780,792	15,035,283	934,341.2	36,722	10,353,925
GAS	Total gas supply	178,096.4	4216	1,641,696	22,447.98	50	422,860

exhibits the descriptive statistics of all the variables between the treatment group and control group. The mean of CO₂ emissions in treatment group and control group are 60.877 and 27.612 (million tons). This shows that the CO₂ emission of the treatment group is more than twice that of the control group. Except that the primary industry as a percentage of the GRP and secondary industry as a percentage of the GRP in the treatment group are less than those in the control group, the values of the other eight control variables are greater in the treatment group than those in the control group. The main reason is that most of the cities in the treatment group are economically developed and densely populated tourist cities.

3. Results and Discussion

3.1. Participation of Tourism Sector can Promote Tourism Carbon Emission Reduction

After controlling the factors (e.g., economic development, land use, and population;

Table 3. Estimation results of the effect of the tourism low-carbon policy on carbon dioxide emission based on PSM-DID.

Variables	Coef.	St.Err.	t-Value	[95% Conf	Interval]
Treat × Post	−1.622 ***	0.453	−3.580	−2.516	−0.727
pop	0.004	0.003	1.610	−0.001	0.009
lgdp	0.243	0.298	0.820	−0.346	0.832
land	0.003	0.003	1.070	−0.003	0.009
gca	0.015	0.013	1.160	−0.011	0.041
pi	9.172	11.509	0.800	−13.569	31.912
si	9.208	11.505	0.800	−13.526	31.941
ti	9.180	11.505	0.800	−13.553	31.913
ie	−0.0005	0.0005	−0.91	−0.002	0.001
ec	8.8 × 10−8	9.2 × 10−8	0.97	−9.3 × 10−8	2.7 × 10−7
gas	−3.2 × 10−6	3.6 × 10−6	−0.91	−0.00001	3.8 × 10−6
Year fixed effect	YES
City fixed effect	YES
Constant	−918.101	1150.913	−0.80	−3192.195	1355.994
R-squared	0.9987		Number of obs	608

Note: *** denote significance levels of 1%.

) that affect CO₂ emissions, we compared the change in carbon emissions between tourism-developed cities and other cities. If the participation of tourism departments in the formulation and implementation of low-carbon tourism policies leads to higher carbon emission reduction in tourism-developed cities than in other cities, the participation of tourism departments promotes the reduction of carbon emissions in the tourism industry. Otherwise, the participation of the tourism sector has no impact on the reduction of carbon emissions in the tourism industry.

As shown in Table 3, the participation of tourism departments in the formulation and implementation of low-carbon tourism policies leads to 1.622 million more tons (1% significance level) of carbon emission reduction in tourism-developed cities than in other cities, suggesting that the participation of the tourism sector can promote the reduction of carbon emissions in the tourism industry.

3.2. Robustness Checks

3.2.1. Results Based on Different PSM Methods

To further verify the robustness of the research results, this paper used a different matching method to control the self-selection bias, which is a from 1 to 4 nearest neighbor matching method. The result using the from 1 to 4 nearest neighbor matching method show that carbon emission reduction policies lead to decreases in CO₂ emissions by the same value (

Table 4. Estimation results of using a from 1 to 4 nearest neighbor matching method.

Variables	Coef.	St.Err.	t-Value	[95% Conf	Interval]
Treat × Post	−1.622 ***	0.4527	−3.58	−2.515939	−0.7271445
pop	0.004	0.0025	1.61	−0.0009249	0.0089765
lgdp	0.243	0.2980	0.82	−0.3457007	0.8320372
land	0.003	0.0030	1.07	−0.0027688	0.0092538
gca	0.015	0.0130	1.16	−0.0106069	0.0406528
pi	9.172	11.5091	0.8	−13.569230	31.912450
si	9.208	11.5053	0.8	−13.525690	31.941100
ti	9.180	11.5051	0.8	−13.552710	31.913220
ie	−0.0005	0.0005	−0.91	−0.0015682	0.0005770
ec	0.0000001	0.0000001	0.97	−0.0000001	0.0000003
gas	−0.000003	0.0000036	−0.91	−0.0000103	0.0000038
Year fixed effect	YES
City fixed effect	YES
Constant	−918.1008	1150.913	−0.80	−3192.195	1355.994
R-squared	0.9987		Number of obs		608

Note: *** denote significance levels of 1%.

), except for the slight difference in the size of the standard error. Therefore, the analysis is robust.

3.2.2. Placebo Test

As a second robustness check, we constructed a counterfactual test to check whether our results were driven by other unobserved factors. In doing so, we selected a different date (2016) in lieu of the real start date of the tourism low-carbon policy, specifying the interaction term between the treatment variable and the different start time variable as $Trea{t}_{it}\times Post\_{16}_{it}$. The tourism low-carbon policy could only take effect after 2017, while the $Trea{t}_{it}\times Post\_{16}_{it}$ coefficient could reflect the impact of the tourism low-carbon policy at the wrong time. If the estimated coefficient of $Trea{t}_{it}\times Post\_{16}_{it}$ was significant and had the same sign as that of the true tourism low-carbon policy shown in Table 3, we can conclude that CO₂ emission reduction in pilot cities cannot be mainly attributed to the tourism low-carbon policy and the robustness of the DID model is inadequate. According to the regressions shown in

Table 5. Estimation results of placebo test.

Variables	Coef.	St.Err.	t-Value	[95% Conf	Interval]
Treat×Post_16	−0.371	0.340	−1.09	−1.0420990	0.3008057
pop	0.003	0.003	1.14	−0.0022309	0.0083578
lgdp	0.009	0.318	0.03	−0.6195018	0.6379815
land	0.003	0.003	0.91	−0.0032625	0.0088183
gca	0.017	0.014	1.21	−0.0106673	0.0444880
pi	7.782	11.810	0.66	−15.554510	31.117820
si	7.830	11.806	0.66	−15.498630	31.157660
ti	7.801	11.806	0.66	−15.525470	31.127840
ie	−0.001	0.001	−1.04	−0.0018481	0.0005732
ec	−2.13 × 10−8	1.08 × 10−7	−0.2	−0.0000002	0.0000002
gas	−4.42 × 10−6	3.57 × 10−6	−1.24	−0.0000115	0.0000026
Year fixed effect	YES
City fixed effect	YES
Constant	−776.117	1181.29	−0.66	−3110.234	1558
R-squared	0.9987		Number of obs	608

, the coefficient of $Trea{t}_{it}\times Post\_{16}_{it}$ was not significantly negative at a 1% level. That is to say, the effect of the CO₂ emission reduction policy was evident.

3.3. Reasons Why the Participation of Tourism Sector can Promote Tourism Carbon Emission Reduction

Low-carbon tourism refers to the control of greenhouse gas emissions in tourism development, namely the reduction of greenhouse gas emissions in tourism development through the development of low-carbon tourism transportation, low-carbon tourism accommodation, low-carbon tourism catering, and various low-carbon tourism activities. The participation of the tourism sector can contribute to carbon emission reduction by creating low-carbon tourism attractions, which can be divided into tourism facilities and tourism culture. Tourism facilities include the important carbon sink of natural resources (wetland, ocean, and forest) [31,32] and artificial facilities (low-carbon buildings, transportation, and tourism activity products) [33,34]. The low-carbon lifestyle can be promoted in the form of cultural tourism through means such as a low-carbon demonstration zone [35,36].

(1)

Low-carbon tourism transportation: The tourism sector could adopt low-carbon vehicles to replace traditional high-emission vehicles. For example, it can create more tourist attractions for cycling tours and hiking [33], providing electric cars instead of traditional high-emission vehicles [37].

(2)

Low-carbon buildings: In recent years, disasters have increased the carbon emissions of China’s tourism industry. For example, the provinces of Henan, Sichuan, Shanxi, and Hebei were hit by severe rainstorms and floods in July 2021. Severe rainstorms and floods hit the provinces of Hubei and Shaanxi in August 2021. In September, a magnitude 6.0 earthquake occurred in the Luxian county of the Sichuan Province and a mud rock slide occurred in the Tianquan County of Ya’an, Sichuan Province. As the global temperature continues to rise, more extreme climate events are expected to occur in the future [17]. Rebuilding after extreme disasters, which often damages local cultural relics and tourist facilities, generates new carbon emissions. The tourism sector is thus directly involved in guiding the reconstruction of and deciding whether to adopt low-carbon solutions.

(3)

Low-carbon tourism-related food, beverages, and commodities: The indirect emissions from the production of food, beverages, and commodities contributes to 35% of the annual CO₂ emissions from the tourism sector (1.6 billion tons of CO₂-e) [7]. If the tourism sector were to purchase low-carbon products, carbon emissions in the sectors producing and processing these commodities would be reduced.

(4)

The promotion of a low-carbon lifestyle: The realization of low-carbon development ultimately depends on people’s actions, which are governed by their values. Establishing low-carbon values is helpful for the promotion of a low-carbon lifestyle (e.g., low-carbon consumption, family life, office life, and relaxation). The tourism sector could instill low-carbon values in tourists by establishing low-carbon popular science attractions and low-carbon tourism demonstration areas [35,36].

3.4. Policy Implications

We found that the participation of the tourism sector promotes carbon emission reduction in the tourism industry and further explored the reasons why such promotion occurs. How the tourism sector can better promote low carbon emissions is an urgent topic of discussion.

First, the complete calculation of tourism carbon emissions is needed. Catering, commodities, and manufacturing are also important sources of carbon emission in the tourism industry [7]. However, the total emission estimation of China’s tourism only involves the carbon emissions of the transportation and accommodation sectors and excludes the carbon emissions of the food, beverage, and commodity manufacturing sectors [18]. Undoubtedly, such carbon emission accounting requires the cooperation and participation of the tourism sector.

The tourism industry should use the market to promote a carbon peak and use technology to achieve carbon neutrality (Figure 2). If all carbon emissions are charged, the cost of carbon emissions is already included in the decision-making process. Based on a reasonable carbon price, the market can maximize the utilization benefit of existing resources, that is, to maintain the rapid and healthy development of tourism and realize the carbon emission peak at the current technological level [17].

A carbon price can be implemented in two forms: a carbon tax or cap-and-trade [17]. The cap-and-trade refers to capping the total carbon emissions of different regions, sectors, enterprises, or individuals. If carbon emissions exceed a certain level, the excess emissions should be bought from other regions, sectors, enterprises, or individuals [38]. China applied the cap-and-trade scheme Emission Trading Scheme (ETS) to carbon-intensive industries (e.g., the power sector) on 16 July 2021. Meanwhile, ETS was piloted in a few provinces (e.g., Guangdong, Chongqing, and Hubei) [18]. As mentioned above, tourism relies heavily on carbon-intensive industries. Therefore, the implementation of ETS has had a significant effect on carbon emission reductions in China’s tourism industry [39]. However, the participation of tourism enterprises in ETS is low and only a few large hotels participate in the ETS system because most tourism enterprises are small- and medium-sized enterprises with low emissions [12]. The full participation of the tourism industry in ETS requires coordination and planning by the tourism sector.

Market measures can only help achieve a carbon peak; to achieve the carbon neutrality of tourism, we need to solve a number of technical issues. As mentioned above, the development of the tourism industry relies heavily on carbon-intensive industries. The major sources of the carbon emissions in tourism are transportation (planes, cars, and trains), electric power (required to operate the hotels and restaurants), and the burning of fossil fuels (involved in the production of goods as well as animal husbandry and aquaculture for food processing and retail supply). The basis for achieving zero emissions of CO₂ in the transport, power, and manufacturing sectors is the decarbonization of electricity. The decarbonization of electricity can be realized if a grid-level energy storage battery can be invented [32]; if the incidence of nuclear power can be reduced to zero using artificial intelligence, machine learning, and other advanced methods [40]; if the materials can be created to prevent hydrogen leakage [41,42]; and if the cost of these technologies is comparable to that of conventional fossil fuels. The carbon emissions of the animal husbandry and aquaculture sectors related to the tourism industry mainly include the greenhouse gas emissions from chemical fertilizer, livestock, and poultry. If gene technology is used to create crops that fully absorb nitrogen fertilizer and cell multiplication can be developed to produce plant-based meat substitutes that taste like real meat, then carbon neutrality can be realized in the animal husbandry and aquaculture sector. If there is a disruptive technology that converts CO₂ into starch [43] that can be commercialized, the goal of carbon neutrality in tourism can be achieved while addressing the global food crisis.

Note that the market and technology are not independent (Figure 2). When a zero-carbon technology is still in its infancy, social capital is not a risk to investment, so the government needs to invest in technology. Once the technology is mature enough to realize the possibility of industrialization, social capital is bound to be injected in large quantities to further promote the implementation of the technology. The adoption and popularization of technology also produce a return on the investment of social capital (Figure 2).

Overall, the tourism sector should actively participate in emission reductions in greenhouse gases. In particular, the tourism sector should actively participate in ETS, reduce the level of hotel energy consumption, encourage participation in the forestry carbon sink, and integrate low-carbon elements into tourism projects. In less developed western regions (e.g., the Shaanxi Province), tourism can serve as a low-carbon alternative to carbon-intensive industries. In addition, tourism-related transportation vehicles (cars and buses) should adopt electric vehicles as much as possible and actively promote the electrification of civil aviation aircrafts, which can pave the way for the application of zero-carbon technology in tourism after the realization of the zero carbonization of electricity.

Practically, the results of our study suggest that the tourism sector should change its role and actively participate in the carbon emission reduction of tourism, instead of waiting for the emission reduction measures of other sectors to passively reduce tourism-related carbon emissions. Theoretically, future studies should focus on how the tourism sector could more effectively participate in the carbon emission reductions; few studies have tried to uncover how the tourism sector could effectively participate in carbon emission reductions. As we found that the participation of tourism can promote low carbon emissions, future studies should try to uncover how the tourism sector could more effectively participate in the carbon emission reductions.

4. Conclusions

Because aviation, accommodation, catering, and other industries involved in the tourism sector are all independent industries, it has been unclear whether the participation of the tourism sector can facilitate carbon emission reduction. In this study, through a quantitative analysis, we found that the participation of the tourism sector can promote carbon emission reduction in the tourism industry. In particular, the participation of tourism departments in the formulation and implementation of low-carbon tourism policies leads to a 1.622 million tons (1% significance level) higher carbon emission reduction in tourism-developed cities than in other cities. The participation of the tourism sector can promote carbon emission reduction in the sectors of transportation, construction, and commodity production. It can also promote a low-carbon lifestyle. Finally, we suggest that the tourism industry use the market to promote a carbon peak and use technology to achieve carbon neutrality.