Research on the Carbon Footprint of Rural Tourism Based on Life Cycle Assessment: A Case Study of a Village in Guangdong, China

Abstract

In the context of China’s “dual carbon” goals and rural revitalization strategy, scientifically assessing the carbon footprint of rural tourism is essential for promoting the sustainable development of the tourism sector. This study presents the first case analysis of the rural tourism carbon footprint in Guangdong Province, using Village B as a representative example. A tourism carbon footprint model for village B was developed using the life cycle assessment (LCA) method. Based on empirical survey data, the tourism carbon footprint of Village B in 2024 was estimated at 7731.23 t, with a per capita carbon footprint of 38.656 kg/p/a. Among the contributing sectors, transportation accounted for the largest share (85.18%), followed by catering (6.93%) and accommodation (5.10%). As an ecotourism-oriented rural destination, Village B exhibited a relatively low carbon footprint from recreational activities. To facilitate the low-carbon transition of rural tourism in the study area and accelerate progress toward the “dual carbon” targets, it is recommended to optimize public transport infrastructure, promote green mobility, enhance the energy efficiency of rural dining and accommodation, and raise awareness of low-carbon tourism.

1. Introduction

As a central component of China’s rural revitalization strategy, rural tourism has capitalized on rich natural resources and cultural heritage to become a major driver of rural economic growth. In 2019, rural tourism in China recorded 3.09 billion visits, accounting for over half of all domestic tourism trips. By 2022, rural tourism represented 89.4% of total domestic tourist visits [1]. In the post-pandemic era, rural tourism has experienced rapid growth, but it has also posed significant challenges to environmental sustainability, particularly in terms of carbon emissions. In recent years, global efforts to advance sustainable tourism have intensified. The European Union’s “Green Deal” explicitly outlines emission reduction targets for the tourism sector, while China’s 14th Five-Year Plan emphasizes the transition toward green and low-carbon tourism. Following the introduction of the Paris Agreement and the Glasgow Declaration, the tourism industry’s responsibility in reducing emissions and achieving carbon neutrality has become increasingly prominent. Notably, the Glasgow Declaration aims to halve tourism-related carbon emissions by 2030 and achieve net-zero emissions by 2050 [2]. These objectives necessitate evidence-based actions in measuring, managing, and mitigating tourism-related carbon footprints [3].

As the global pursuit of carbon neutrality accelerates, measuring and reducing tourism-related carbon footprints have emerged as key research priorities. The tourism carbon footprint, a crucial indicator of the environmental impact of tourism activities, directly reflects the extent and intensity of energy consumption and greenhouse gas emissions across the tourism lifecycle. Within the framework of China’s “dual carbon” targets, examining the carbon footprint of rural tourism holds important theoretical and practical implications. Quantifying rural tourism’s contribution to carbon emissions facilitates the identification of key processes and emission hotspots. Moreover, it provides a scientific foundation for formulating low-carbon tourism policies, optimizing resource allocation, and raising tourists’ environmental awareness. Therefore, accurately measuring and effectively managing the carbon footprint of rural tourism has become essential to promoting rural revitalization and sustainable development.

The tourism carbon footprint refers to the amount of carbon dioxide equivalent emissions generated both directly and indirectly by tourism activities [4]. It encompasses the entire travel process—from departure to destination—including transportation, accommodation, dining, shopping, entertainment, and other related activities that contribute to both direct and indirect carbon emissions. According to previous studies, the calculation of tourism’s carbon footprint primarily includes emissions from transportation, accommodation, dining, sightseeing, entertainment, shopping, and other aspects [4,5,6,7]. The calculation scope, influencing factors, and key assessment areas are summarized in

Table 1. Accounting scope, influencing factors, and accounting focus of tourism carbon footprint.

Accounting Segments	Accounting Scope	Influencing Factors	Key Calculation Focus
Transportation segment	The carbon footprint generated by tourists’ round-trip transportation to and from the destination.	Transportation methods (public transportation such as airplanes, trains, buses, etc. [8,9,10,11], self-driving tourism [12,13]), travel distance [14], the development level of the destination’s public transportation network (public transportation usage rate) [15], transportation connectivity [13], and types of transportation fuel [16,17], etc.	Air travel (especially long-distance travel) and private transportation (such as self-driving cars).
Accommodation segment	The carbon footprint generated by tourists’ accommodation activities during their trip, including the carbon footprint of lighting, heating, cooling, washing, and other services in the accommodation facilities.	Accommodation type [15], hotel rating [13], energy utilization structure [18], etc.	High-end hotels (higher than budget accommodations, with higher energy consumption and more complex services).
Catering segment.	The carbon footprint generated by tourists’ dining activities during their trip, including the carbon footprint associated with food production, processing, storage, transportation, consumption, and the energy consumption process in dining establishments.	Types of ingredients [19], production methods [19], energy utilization structure [20], etc.	High-consumption models (such as luxury dining), meat and seafood (higher emissions than plant-based foods), industrial production, and local organic food have different emissions. Local, minimally processed foods have a lower carbon footprint.
Sightseeing segment	The carbon footprint generated by tourists’ sightseeing activities.	Energy consumption of sightseeing facilities [21], etc.	High-energy consumption sightseeing facilities.
Entertainment segment	The carbon footprint generated by tourists’ recreational activities, such as cultural events, outdoor sports, and other leisure activities.	The type and scale of activities [13,14,22,23], as well as the energy consumption of equipment [21], etc.	High-energy facilities (such as theme parks and ski resorts) have significantly higher carbon emissions compared to nature-based ecological tourism. Indoor activities and those involving mechanical equipment generate higher emissions than natural hiking.
Shopping segment	The carbon footprint generated by tourists’ shopping activities during their trip, including the carbon footprint associated with the production, processing, storage, transportation of tourism products, and the energy consumption of services provided by retail stores.	Product types, production methods, transportation distances and packaging materials, as well as the types of energy used by retail stores in providing services [14,24,25], etc.	The carbon footprint of industrialized products transported over long distances is usually higher than that of locally crafted handmade products.
Other segments	The carbon footprint generated by tourism waste management (such as waste transportation and disposal) and the construction of tourism infrastructure (such as scenic area development and transportation facility construction).	Types of waste, quantity, disposal methods, energy consumption for transportation and disposal, and types of infrastructure construction [26], etc.	-

.

Existing research on tourism carbon footprints primarily focuses on broader geographical scales, including global [27], national [22,28,29], provincial [30,31], municipal [5,6], and regional [32,33] levels. However, there is a notable lack of carbon emission data at the microscale, particularly for rural tourism. Empirical studies focusing on specific rural areas remain limited, particularly in resource-rich regions such as Guangdong. Existing studies have yet to establish a standardized measurement framework for rural tourism carbon footprints [26,34,35,36], resulting in ambiguous measurement boundaries. Internal tourism-related transportation emissions are often considered, whereas external transportation and indirect emissions, such as those from waste treatment, are frequently overlooked. Moreover, many rural tourism destinations lack reliable statistical infrastructure. Emission coefficients are typically derived from regional or industry-wide averages, limiting their accuracy in representing specific rural contexts.

The life cycle assessment (LCA) method enables a comprehensive evaluation of rural tourism’s carbon footprint. To improve the accuracy of carbon footprint calculations, this study presents the first case analysis of rural tourism carbon footprint in Guangdong Province. Guangdong Province, a major economic region with a rapidly growing rural tourism sector, was selected as the case study area. Village B, a representative site with rapidly developing rural tourism, was selected to develop a more comprehensive carbon footprint model using the LCA method. The study also explores practical data collection methods, systematically analyzes emission sources and characteristics, and examines the potential and challenges of aligning rural tourism with the “dual carbon” goals and rural revitalization. It further seeks pathways for the low-carbon transformation of rural tourism consistent with the “dual carbon” targets. This research advances the theoretical understanding of rural tourism carbon footprints and fills a critical research gap in Guangdong Province by providing empirical evidence for sustainable development. It also establishes an empirical foundation for integrating the “dual carbon” goals with rural revitalization strategies, offering practical insights for policymakers and community stakeholders.

2. Materials and Methods

2.1. Study Area

Village B is located in Town A, Conghua District, Guangzhou, Guangdong Province, China, in the central region of the province, northeast of Guangzhou City. It is geographically positioned at 113°17′–114°04′ E and 23°22′–23°56′ N [37]. The village covers a total administrative area of 503.39 hectares. It is approximately one hour by car from the center of Conghua with convenient regional transportation. As shown in Figure 1, Village B is situated near several natural scenic sites, including Paitan Phoenix Mountain Forest Park, Baishuizhai Scenic Spot, Dafengmen Forest Park, Conghua Hot Spring Scenic Spot, Shimen National Forest Park, and Nankun Mountain, indicating a pronounced tourism agglomeration effect.

In 2016, Village B was designated as a tourism-oriented village emphasizing sightseeing and leisure as part of the third batch of provincial-level new rural demonstration areas in Guangzhou. With a forest coverage rate of 97%, Village B features five distinct ecological elements: mountains, springs, forests, streams, and rocks. Leveraging its natural and cultural resources, the village has developed a diverse portfolio of rural tourism offerings, including specialty cuisine, recreational activities, agricultural experiences, performing arts, and unique shopping, forming an integrated cultural-tourism complex. The rapid growth of rural tourism has contributed to increased household income and improved industrial efficiency. The per capita annual income of villagers increased from 16,200 yuan in 2016 to 38,000 yuan in 2022 [38], representing a 134.6% increase. Village B has received seven national-level honors, such as “China’s Beautiful Leisure Village”, ”National Forest Village”, “Key National Rural Tourism Village”, and “National AAA Tourism Scenic Area”, along with twelve provincial-level recognitions, including “Guangdong Province Cultural and Tourism Specialty Village” and “Top Ten Beautiful Villages in Guangdong Province”. Additionally, it has earned eleven city-level accolades and was selected as one of the “Top Ten Innovative Cases of Rural Party Building in Guangdong”. The village has attracted national-level recognition and has become a benchmark for rural revitalization in both Guangdong Province and China as a whole. It has also been identified as a representative case in the “Project of High-quality Development in Hundred Counties, Thousands Towns and Ten Thousand Villages”.

2.2. Data Source

Survey Design: A structured questionnaire was designed to collect data on tourists’ transportation modes (e.g., private car, bus), sightseeing behaviors, travel origins, accommodation conditions, and participation in recreational activities. The questionnaire comprised both closed-ended questions (e.g., options for transportation mode) and open-ended questions (e.g., details on tourist activities). To ensure content validity, a pilot survey was conducted, and ambiguous questions were revised accordingly. The survey yielded data on round-trip transportation distances, transportation modes, energy sources, and levels of energy consumption. Carbon footprints associated with transportation and sightseeing were calculated using corresponding transportation energy coefficients.

Survey Distribution and Collection: A random sampling method was adopted, and questionnaires regarding the rural tourism carbon footprint of Village B were distributed from January to October 2024 (see Appendix A). The survey period encompassed both peak tourist seasons (e.g., holidays) and off-peak periods. Face-to-face interviews were conducted to enhance the response rate and ensure timely data collection. A total of 740 questionnaires were distributed, and 690 valid responses were obtained, yielding a response rate of 93.2%. The survey was conducted in accordance with ethical standards, and all respondents participated voluntarily.

Field Investigation: Between January and October 2024, field investigations were conducted (see Appendix B: Energy-related Interviews in the Rural Tourism Destination of Village B). Data on energy consumption for accommodation, dining, and shopping were gathered through interviews with 23 homestays, 10 restaurants, and 21 shops, supplemented by electricity bills and operation records. Waste carbon footprints were estimated through interviews with personnel from the local waste management department. All fieldwork complied with ethical research standards, and participation was entirely voluntary.

Standard Data: Standard data were obtained from official sources, including the “China Energy Statistical Yearbook (2023)”, General rules for calculation of the comprehensive energy consumption (GB/T 25890-2020) [39], and relevant academic literature.

2.3. Methods

A comprehensive literature review suggests that a tourist’s carbon footprint spans the entire tourism life cycle, including transportation, accommodation, dining, entertainment, shopping, and waste disposal. Consequently, this study primarily quantifies the carbon footprints of tourism transportation, accommodation, activities, and dining from the tourists’ perspective, using a bottom-up approach. The life cycle assessment method was employed to evaluate and quantify the rural tourism carbon footprint in Village B. Drawing on the carbon footprint measurement methods proposed by scholars such as Xiao Jianhong [32], Xie Yuanfang [40], Luo Chunlin [41], and Ding Yulian [42], this study establishes a more comprehensive model for assessing the rural tourism carbon footprint in Village B, as elaborated in Section 2.3.1, Section 2.3.2, Section 2.3.3, Section 2.3.4, Section 2.3.5, Section 2.3.6 and Section 2.3.7. All symbols and their meanings are provided in Appendix C.

2.3.1. Tourism Transportation Carbon Footprint Model

This section quantifies the direct and indirect carbon footprints resulting from tourists’ energy consumption during transportation to and from rural tourism destinations.

C₁ = ∑(N_a × D_a × ρ_a) × 2

(1)

where C₁ denotes the carbon footprint of tourism transportation (kg); N_a denotes the total number of tourists using transportation mode a (p); D_a denotes the distance traveled by transportation mode a (km); and ρ_a denotes the carbon emission factor per person per kilometer for transportation mode a (kg CO₂/p/km), as presented in

Table 2. Carbon emission coefficient of different modes of transport.

Transportation Mode	Carbon Emission Coefficient	Data Sources
Coaches	0.018kg CO2/p/km	Wang et al. [43]
Subway	0.027kg CO2/p/km	Wang Xiaojuan [44]
Taxi	0.25kg CO2/p/km	Xiao xiao et al. [45]
Own car	0.25kg CO2/p/km
Bus	0.033kg CO2/p/km	Yang Xiuling [46]

. To simplify the analysis, it is assumed that tourists use the same mode of transportation for both outbound and return journeys. Accordingly, the derived formula quantifies the carbon footprint associated with tourists’ transportation between their origin and destination.

2.3.2. Tourism Accommodation Carbon Footprint Model

This section quantifies the direct and indirect carbon footprints associated with tourist accommodation activities. These primarily include carbon emissions resulting from energy consumption across various accommodation types at the destination, encompassing indirect carbon emissions from electricity use and direct carbon emissions from fossil fuel consumption. In rural tourism destinations, accommodation types typically include homestays, inns, and budget hotels. Based on the equation “Carbon emissions = Energy consumption × Emission factor”, the carbon emission calculation method for rural tourism accommodation is developed, as detailed in the following equations:

C₂ = ∑C_2b

(2)

C_2b = ∑E_bi × C_i × EF_ce

(3)

where C₂ denotes the carbon footprint of tourism accommodations (kg); C_2b denotes the carbon footprint of the b-th accommodation facility (kg); E_bi denotes the energy consumption of the i-th energy type at the b-th accommodation facility; C_i denotes the conversion factor for converting the i-th type of energy to standard coal, as presented in

Table 3. Reference coefficients of standard coal for various energy sources.

Energy Type	Average Lower Heating Value	Standard Coal Conversion Factor
Liquefied Petroleum Gas	50,179 kJ/(12,000 kcal)/kg	1.7143 kg standard coal per kg
Natural Gas	38,931 kJ/(9310 kcal)/kg	1.3300 kg standard coal/m3
Electricity (Equivalent)	3596 kJ/(860 kcal)/kg	0.1229 kg of standard coal per kilowatt-hour.
Firewood	16,726 kJ/(4,000 kcal)/kg	0.571 kg of standard coal per kg

Data sources: General Principles for Calculation of Comprehensive Energy Consumption (GB/T 2589-2020) and China Energy Statistical Yearbook (2023).

; and EF_ce denotes the CO₂ emission factor for standard coal, with an empirical value of 2.45 kg CO₂/kg standard coal [47].

2.3.3. Tourism Catering Carbon Footprint Model

This study primarily quantifies the carbon footprint resulting from energy consumption in the catering sector during the provision of food and services to tourists, while it does not incorporate upstream emissions from agricultural production of food consumed by tourists, mainly due to limited local primary data availability.

C₃ = ∑E_cj × C_j × EF_ce

(4)

where C₃ denotes the carbon footprint of tourism catering (kg); E_cj denotes the energy consumption of the j-th type of energy at the c-th restaurant; C_j denotes the conversion factor for converting the j-th energy type to standard coal, as presented in Table 3; and EF_ce denotes the CO₂ emission factor for standard coal, with an empirical value of 2.45 kg CO₂/kg for standard coal.

2.3.4. Tourism Recreation Carbon Footprint Model

This study quantifies the direct and indirect carbon footprints associated with tourists’ participation in traditional rural festivals, folk activities, outdoor sports, agricultural production experiences, and other recreational activities in rural tourism destinations.

C₄ = ΣE_d × Q_d

(5)

where C₄ denotes the carbon footprint of tourism recreational activities (kg); E_d denotes the number of participants in recreational activity d (p); and Q_d denotes the carbon emission factor for energy consumption for recreational activity d (kg/p).

2.3.5. Tourism Sightseeing Carbon Footprint Model

This study quantifies the direct and indirect carbon footprints associated with tourists’ vehicle travel for sightseeing of natural ecosystems, folk culture, and agricultural production activities in rural tourism destinations.

C₅ = ∑(N_e × D_e × ρ_e) × 2

(6)

where C₅ denotes the carbon footprint of tourist sightseeing (kg); N_e denotes the total number of tourists using transportation mode e (p), D_e denotes the distance traveled by transportation mode e (km); and ρ_e denotes the carbon emission factor per person per kilometer for transportation mode e (kg CO₂/p/km), as presented in Table 2.

2.3.6. Tourism Shopping Carbon Footprint Model

This study primarily quantifies the indirect carbon footprint associated with energy consumption in the production of goods (e.g., agricultural products) and the provision of retail services during shopping activities.

C₆ = ∑E_fk × C_k × EF_ce

(7)

where C₆ denotes the carbon footprint of tourism shopping (kg); E_fk denotes the energy consumption of the k-th energy type at the f-th retail store; C_k denotes the conversion factor for converting the k-th energy type to standard coal, as presented in Table 3; and EF_ce denotes the CO₂ emission factor for standard coal, with an empirical value of 2.45 kg CO₂/kg standard coal.

2.3.7. Tourism Waste Carbon Footprint Model

This study quantifies the direct and indirect carbon footprints associated with waste disposal throughout the tourism process, accounting for energy consumption from waste transportation and incineration.

C₇ = ∑(L_n × ρ_n × W_n × EF_ce) + ∑(IW_n × CCW_n × FCF_n × EF_n × 44/12

(8)

where C₇ denotes the carbon footprint of waste disposal at the tourism site (kg); L_n denotes the transportation distance for the n-th types of waste (km); ρ_n denotes the energy consumption factor for transporting the n-th waste type (kgce/t/km), as presented in

Table 4. The energy consumption of garbage transportation for different types of garbage.

Waste Transportation Categories	Energy Consumption Factor for Transporting Waste	Data Sources
Kitchen waste transportation	0.10 kgce/t/km	He Jiani et al. [49]
Other waste transportation	0.13 kgce/t/km

; W_n denotes the mass of the n-th waste type transported (t); EF_ce denotes the CO₂ emission factor for standard coal, with an empirical value of 2.45 kg CO₂/kg standard coal; IW_n denotes the mass of the n-th solid waste type incinerated (t); CCW_n denotes the carbon content in the dry matter of the n-th waste type (%); FCF_n denotes the proportion of mineral carbon in the total carbon of the n-th waste type (%); EF_n denotes the complete combustion efficiency of the incinerator for the n-th solid waste type(%); and 44/12 is the conversion coefficient of carbon to carbon dioxide. According to the IPCC Guidelines for National Greenhouse Gas Inventories, the carbon content of solid waste (CCWn) is 40%, the proportion of mineral carbon in total carbon (FCFn) is 40%, and the combustion efficiency (EFn) is 95% [48].

2.3.8. Assumptions and Limitations of the Model

The rural tourism carbon footprint model is based on the following assumptions:

(1)

Symmetric Round-Trip Transportation: It is assumed that tourists use the same mode of transportation for both the inbound and outbound journeys. Although this simplification facilitates the calculation process, it may lead to an underestimation of emissions if different transportation modes are used for each leg (e.g., taxi for arrival and bus for departure).

(2)

Static Energy Structure: Energy conversion coefficients (e.g., electricity emission factor) are derived from the 2023 national average and do not account for dynamic regional variations in energy structure.

(3)

Seasonal Homogeneity: The model assumes that tourist behaviors (e.g., transportation preferences) remain consistent across seasons, potentially overlooking variations between peak and off-peak periods.

In addition, dynamic changes were also taken into account, such as the effects of key variables—including travel distance, energy structure, and the number of tourists—on total carbon emissions. Detailed scenario simulations are provided in Appendix D.

3. Results

3.1. Descriptive Statistical Analysis

Table 5. Basic statistics of tourist samples in Village B.

Feature	Type	Frequency	Percentage/%
Gender	Male	283	41.0
	Female	407	59.0
Age	<18	105	15.2
	18–25	63	9.1
	26–30	104	15.1
	31–40	163	23.6
	41–50	70	10.1
	51–60	136	19.7
	>60	49	7.1
Education level	Junior high school and below	141	20.4
	High school/vocational school/technical school	83	12.0
	Junior college	47	6.8
	Undergraduate	296	42.9
	Master and above	123	17.8
Mode of travel	Solo self-guided tour	104	15.1
	Group or family/friends self-guided tour	379	54.9
	Travel agency-organized tour	148	21.4
	Company-organized tour	59	8.6
Length of stay	1 day	456	66.1
	2 days	201	29.1
	3 days	33	4.8

presents the statistical summary of the basic demographic characteristics of the tourist sample in Village B. The gender distribution is relatively balanced, with male tourists comprising 41.0% (n = 283) and female tourists comprising 59.0% (n = 407). The age distribution is divided into seven groups: under 18, 18–25, 26–30, 31–40, 41–50, 51–60, and over 60 years old. The largest proportion of tourists falls within the 31–40 age group, accounting for 23.6% (n = 163). Educational attainment is classified into five levels: junior high school and below, high school/vocational school/technical school, junior college, undergraduate, and master’s and above. The largest share is represented by undergraduates, accounting for 42.9% (n = 296). Travel modes are grouped into four categories: solo self-guided tour, group or family/friends self-guided tour, travel agency-organized tour, and company-organized tour. The most common mode is a group or family/friends self-guided tour, accounting for 54.9% (n = 379). Regarding length of stay, rural tourists in Village B tend to make short visits, with the majority staying for only one day (66.1%, n = 456).

To provide a comprehensive assessment of the carbon footprint of rural tourism activities in Village B, the following sections analyze seven core sectors: transportation, accommodation, catering, recreation, sightseeing, shopping, and waste management. Each component is quantified based on detailed survey data and standardized emission factors, offering an integrated overview of tourism-related carbon emissions in 2024.

3.2. Tourism Transportation Carbon Footprint

Data from a total of 690 completed questionnaires were collected through a visitor survey, capturing data on tourists’ departure locations and transportation choices when visiting Village B. Owing to its geographical location, the majority of tourists traveling to Village B originate from the Pearl River Delta region. Tourists selected from five modes of transportation: coach, private car, bus, subway and taxi, and taxi. The distribution of transportation modes is as follows: 38.99% coach, 54.64% private car, 2.03% public bus, 2.03% subway and taxi, and 2.32% taxi. The corresponding statistical results are presented in Figure 2. Among all respondents, the majority of tourists—632 in total—were from Guangdong Province. Detailed departure locations and transportation modes for tourists from Guangdong Province are illustrated in Figure 3. A total of 49 tourists were from Macau (all traveled by coach), and 9 tourists were from Guangxi Province (specifically from Yuzhou District, Yulin City), all of whom traveled by private car.

Interviews with village committee members and tourism operators revealed that Village B received 200,000 visitors in 2024. By integrating geographic data to determine average travel distances for different transportation modes and applying Equation (1), the tourism transportation carbon footprint of Village B for 2024 was calculated. The total transportation carbon footprint for the village amounted to 6585.22 tons, while the per capita transportation carbon footprint for tourists was 32.926 kg/p/a. Detailed results are presented in

Table 6. Carbon footprint of tourism transportation for different travel modes.

Transportation Mode	Number of Visitors (p/a)	Average Travel Distance (km)	Carbon Emission Coefficient (kg CO2/p/km)	Carbon Footprint (t/a)	Proportion of Carbon Footprint
Coache	77,971.01	129.6	0.018	363.83	5.52%
Private Car	10,9275.36	111.3	0.25	6079.57	92.32%
Bus	4057.97	18	0.033	4.82	0.07%
Subway and Taxi	4057.97	36.6 a 20 b	0.027 c 0.25 d	48.59	0.74%
Taxi	4637.68	38.1	0.25	88.41	1.34%
Total	200,000	-	-	6585.22	100.00%

^a The average travel distance (km) for the subway in the transportation mode of subway and taxi; ^b the average travel distance (km) for the taxi in the transportation mode of subway and taxi; ^c the carbon emission coefficient (kg CO₂/p/km) for the subway; ^d the carbon emission coefficient (kg CO₂/p/km) for taxi.

.

3.3. Tourism Accommodation Carbon Footprint

According to statistics provided by the village committee, there are currently 45 accommodation establishments in Village B, offering a total of 488 beds. Among them, two establishments with more than 40 beds are classified as large homestays, while the remaining 43 establishments with fewer than 40 beds are considered small homestays. Research suggests that small homestays are more prevalent in Village B. Surveys and interviews were conducted to gather information on the types and quantities of energy consumed by these accommodations. On-site interviews were conducted with 23 homestays in Village B (labeled A1 to A23), as presented in

Table 7. Energy consumption and carbon footprint of accommodation industry in Village B (large homestays).

Large Homestays	Number of Beds (Units)	Electricity (kWh/a)	Liquefied Petroleum Gas (kg/a)	Firewood (kg/a)	Converted to Standard Coal Equivalent (kg/a)	Total Carbon Emissions (kg/a)
A1	76	476,580	0	0	58,572	143,501
A2	42	80,769	0	0	9927	24,320
Total	118	557,349	0	0	68,499	167,821

and

Table 8. Energy consumption and carbon footprint of accommodation industry in Village B (small homestays).

Small Homestays	Number of Beds (Units)	Electricity (kWh/a)	Liquefied Petroleum Gas (kg/a)	Firewood (kg/a)	Converted to Standard Coal Equivalent (kg/a)	Total Carbon Emissions (kg/a)
A3	17	37,692	240	1600	5957	14,596
A4	4	2846	75	0	478	1172
A5	6	5692	105	0	880	2155
A6	5	4923	0	0	605	1482
A7	6	5308	90	0	807	1976
A8	5	5092	75	0	754	1848
A9	6	6462	120	0	1000	2450
A10	10	22,384	210	960	3659	8965
A11	18	40,615	390	0	5660	13,867
A12	6	7231	150	0	889	2177
A13	11	16,615	165	740	2747	6731
A14	10	18,154	75	0	2360	5781
A15	6	6230	90	0	920	2254
A16	6	6769	90	0	832	2038
A17	5	3615	90	0	599	1466
A18	17	39,846	450	910	6188	15,161
A19	10	13,500	540	1010	3162	7746
A20	10	18,154	375	1060	3479	8524
A21	10	15,846	195	0	2282	5590
A22	8	6462	180	370	1314	3219
A23	5	5077	0	0	624	1529
Household average value	9	13,739	176	317	2152	5273
Total	181	288,513	3705	6650	45,195	110,729

. The surveyed small-scale homestays primarily relied on electricity, with a relatively low proportion of liquefied petroleum gas usage and the lowest proportion of fuelwood consumption. On average, each small-scale homestay emitted 5273 kg (5.2 tons) of CO₂ per year, mainly attributable to electricity consumption. Based on the calculated average values, the total annual accommodation-related carbon footprint was 394.56 t/a, with a per capita accommodation carbon footprint of 1.973 kg/p/a.

3.4. Tourism Catering Carbon Footprint

The catering industry in Village B is primarily composed of small restaurants and farmhouses. According to statistics from the village committee, there are currently 24 catering establishments in the village. Only one establishment, B1, with more than 20 tables, is classified as a large restaurant. The remaining establishments, comprising small restaurants and farmhouses with relatively few tables, are classified as small restaurants. Field surveys were conducted at 10 catering establishments (labeled B1 to B10), and the types and quantities of energy consumed are presented in

Table 9. Energy consumption and carbon footprint of catering industry in Village B (large restaurants).

Large Restaurants	Electricity (kWh/a)	Liquefied Petroleum Gas (kg/a)	Firewood (kg/a)	Converted to Standard Coal Equivalent (kg/a)	Total Carbon Emissions (kg/a)
B1	149,210	9690	0	34,949	85,626

and

Table 10. Energy consumption and carbon footprint of catering industry in Village B (small restaurants).

Small Restaurants	Electricity (kWh/a)	Liquefied Petroleum Gas (kg/a)	Firewood (kg/a)	Converted to Standard Coal Equivalent (kg/a)	Total Carbon Emissions (kg/a)
B2	22,308	4500	0	10456	25,617
B3	18,615	1980	0	5682	13,921
B4	16,923	1800	0	5166	12,656
B5	14,154	0	8100	6365	15,593
B6	16,308	1875	0	5219	12,785
B7	24,615	5100	0	11,768	28,832
B8	20,000	1650	0	5287	12,952
B9	20,154	2250	0	6334	15,519
B10	26,154	7125	0	15429	37,800
Household average value	20,103	2920	900	7990	19,576
Total	180,931	26,280	8100	71,913	176,188

. The survey also revealed that the food served in Village B’s restaurants is locally sourced from crops grown and livestock raised in the area, resulting in minimal carbon emissions from food transportation. The surveyed restaurants primarily relied on electricity for daily operations, supplemented by liquefied petroleum gas (LPG), while firewood use was nearly eliminated, present only in two establishments. On average, each restaurant emitted approximately 19.58 tons of CO₂ per year, with electricity accounting for the majority of emissions. Based on this estimated average, the total carbon footprint of the catering sector was 535.87 t/a, and the per capita dining carbon footprint for tourists was 2.679 kg/p/a.

3.5. Tourism Recreation Carbon Footprint

The survey revealed that entertainment activities in Village B primarily include fruit picking, fish and shrimp fishing, farm work experiences, hiking on mountain boardwalks, leisure activities at Lixia Book Bar, and nighttime firefly watching. As there are no energy-intensive electric amusement facilities, the carbon footprint of rural tourism entertainment is considered negligible.

3.6. Tourism Sightseeing Carbon Footprint

Based on the tourist questionnaire survey, three modes of transportation within Village B were identified: coach, private car, and walking. The proportion of tourists choosing each mode was 14.64%, 26.81%, and 58.55%, respectively, as shown in Figure 4. Among these, walking is a zero-carbon emission mode, indicating that the sightseeing carbon footprint primarily results from coach and private cars. Most tourists participate in activities such as walking along the creekside plank road, strolling through orchards, and playing in streams. Some tourists travel along the village’s main roads by private car or coach. The primary sightseeing route within the village is approximately 1.8 km in length, resulting in a round-trip distance of around 3.6 km. The average travel distance for private cars and coach is assumed to be twice the length of the village’s main road, totaling 3.6 km. Based on our calculations, the total sightseeing carbon footprint in Village B in 2024 was 50.16 t (as shown in

Table 11. Carbon footprint of sightseeing in Village B.

Sightseeing Mode	Number of Visitors (p/a)	Average Travel Distance (km)	Carbon Emission Coefficient (kg CO2/p/km)	Carbon Footprint (t/a)
Coache	29,275	3.6	0.018	1.90
Private Car	53,623	3.6	0.25	48.26
Walking	117,101	-	-	-
Total	200,000	-	-	50.16

), with a per capita sightseeing carbon footprint of 0.251 kg/p/a.

3.7. Tourism Shopping Carbon Footprint

The survey revealed that there are 21 shopping points in Village B, including 15 small-scale agricultural markets (stall-type vendors) and 6 specialty stores. Products primarily include locally made delicacies such as tofu pudding, herbal jelly, and double-skin milk, as well as locally sourced, ecologically grown produce like lychees, longans, wampee, persimmons, wild ficus hirta, ganoderma lucidum, dried tangerine peel, and other fresh and dried goods. The production of these agricultural products generates relatively low carbon emissions; most emissions arise from the services provided by the stores during the sales process. The 15 small-scale agricultural markets are open-air stalls with no energy consumption, such as electricity, while the 6 specialty stores primarily consume electricity, with minimal variation in usage, averaging 3181 kWh, as shown in Figure 5. According to Equation (7), the total shopping-related carbon footprint of Village B in 2024 was calculated to be 5748 kg (5.75 t), resulting in a per capita shopping carbon footprint of 0.029 kg/p/a. Overall, the carbon footprint associated with shopping in Village B remains relatively low.

3.8. Tourism Waste Carbon Footprint

Tourist-generated waste in Village B primarily consists of kitchen waste, such as leftover food and fruit peels, as well as plastic and paper waste discarded during sightseeing activities. The village is equipped with waste-sorting and recycling stations that categorize waste into recyclables, hazardous waste, kitchen waste, and other waste, as detailed in

Table 12. The garbage disposal situation in Village B.

Types of Waste	Recyclable Waste	Hazardous Waste	Kitchen Waste	Other Waste
Collection time	Appointment time (every Sunday at 17:00)	Appointment time (once a month)	Appointment time (9:30 a.m.)	Appointment time (9:00 a.m.–10:30 a.m.)
Collection unit	Resource recycling station	Sanitation office	Sanitation office	Sanitation office
Collection method	Appointment-based collection	Appointment-based collection	Appointment-based collection	Direct recycling.
Waste flow,	Resource recycling station	Temporary hazardous waste storage point in the district	Seventh Resource Thermal Power Plant	Seventh Resource Thermal Power Plant

.

The survey results suggest that the average amount of kitchen waste generated per tourist is approximately 1.2 kg, while plastic and paper waste contribute around 0.2 kg. The waste-sorting stations in Village B are located 25.3 km from the Seventh Resource Thermal Power Plant, resulting in a round-trip transportation distance of 50.6 km. Based on Equation (8), the carbon footprint associated with rural tourism waste in Village B was calculated.

Table 13. Carbon footprint of garbage transportation in Village B.

Waste Transportation Categories	Waste Transportation Coefficient (kgce/(t·km))	Average Daily Waste Transportation Amount (kg)	One-Way Transportation Distance (km)	Annual Waste Transportation Carbon Footprint (kg)
Kitchen waste transportation	0.10	657.534	50.6	2975.28
Other waste transportation	0.13	109.589	50.6	644.644
Total				3619.924

and

Table 14. Carbon footprint of waste incineration in Village B.

Waste Treatment Categories	Annual Waste Incineration Amount (kg)	Annual Waste Incineration Carbon Footprint (kg)	Waste Treatment Categories	Annual Waste Incineration Amount (kg)
Composting of kitchen waste	240,000	133,760	Composting of kitchen waste	240,000
Incineration of other waste	40,000	22,293	Incineration of other waste	40,000

present the carbon footprints associated with waste transportation and incineration, respectively.

The results suggest that the total waste-related carbon footprint in Village B for 2024 was 159.67 tons, with a per capita rural tourism waste carbon footprint of 0.798 kg/p/a. Of this total, waste incineration contributed the majority (97.73%), while waste transportation accounted for only 2.27%.

3.9. The Total Tourism Carbon Footprint

Based on the calculated carbon footprints of seven components of rural tourism in Village B—tourism transportation, accommodation, dining, entertainment, sightseeing, shopping, and waste—the total rural tourism carbon footprint of Village B for 2024 is estimated at 7731.23 t, with a per capita carbon footprint of 38.656 kg/p/a. As shown in Figure 6, among these seven categories, tourism transportation accounts for the largest share at 85.45%, followed by tourism dining at 6.95% and tourism accommodation at 5.12%. Comparatively, catering and transportation activities were the largest contributors to the total carbon footprint, jointly responsible for over 90% of emissions from tourism in Village B. This pattern underscores the prevailing tendency within the tourism industry for primary services—namely, transport and food—to constitute the principal contributors to carbon emissions. In contrast, emissions from shopping and entertainment were negligible. This comprehensive analysis underscores both the achievements and the persistent challenges in Village B’s transition to a low-carbon tourism model, indicating that targeted measures in high-emission sectors such as catering and transportation will be essential to support Guangdong’s broader sustainable development agenda.

4. Discussion

Among the various components of tourism in Village B, the carbon footprint is distributed in descending order as follows: transportation, dining, accommodation, waste, sightseeing, shopping, and entertainment. Notably, transportation and dining together account for over 90% of the total tourism carbon footprint. The distribution of the carbon footprint across tourism sectors in Village B shows substantial variation, with transportation contributing the largest share—a pattern consistent with findings from numerous previous studies [5,6,15,41,50,51,52,53,54,55]. This dominance is primarily attributed to the tourists’ reliance on energy-intensive modes of transport, particularly automobiles, to access the destination. The carbon footprint of tourism transportation in Village B accounted for as much as 85.45% of the total, while in China, tourism transportation contributes 67.72% of the overall carbon emissions from the tourism sector, making it the primary source of CO₂ emissions in tourism activities [5]. Compared with existing village-level tourism carbon footprint studies, the total carbon footprint of rural tourism in Village B is higher than that of Zhahan Village in Hainan Province in 2019 (1296.3 t) [35] but lower than that of the Huangling Scenic Area in 2016 (9732 t) [34]. The substantial differences in tourism carbon footprints among different villages are largely attributable to variations in the defined calculation boundaries and the scope of included components. In 2024, the per capita tourism carbon footprint in Village B was 38.656 kg/p/a. As a rural tourism destination, this figure exceeds the national average tourism carbon footprint in China (22.7 kg/p/a) [36], as well as those of other rural tourist destinations, including Wuyuan Huangling in Jiangxi Province (13.770 kg/p/a) [34], Zhahan Village in Hainan [35], the provincial average of Shanxi (7.448 kg) [56], and Jigong Mountain Scenic Area in Henan Province (36.644 kg/p/a) [57], while remaining lower than the provincial average of Jiangsu (145.1 kg/p/a) [58]. Overall, the per capita carbon footprint of rural tourism in Village B is comparatively high. This is primarily because Village B is characterized as an ecotourism- and sightseeing-oriented destination, where emissions from entertainment, sightseeing, and shopping are comparatively low. This is consistent with the relatively low proportion of carbon emissions from shopping and recreational activities observed in the four-day tour of Guilin [59]. However, the substantial carbon footprint associated with transportation, accommodation, and dining—particularly due to the heavy reliance on private cars—significantly contributes to the elevated total footprint. Consequently, enhancing the village’s capacity for low-carbon and sustainable development is urgently needed.

Regarding the carbon footprint distribution across different modes of tourism transportation, private cars contribute a substantial 92.32% of the total, followed by coach at 5.52%. This suggests that tourism transportation in Village B primarily relies on high-carbon travel modes. Although 54.64% of tourists travel to Village B by private vehicle, they are responsible for 92.32% of the total tourism transportation carbon footprint. In contrast, 38.99% of tourists use coaches, which contribute only 5.52% of the carbon footprint. This disparity is primarily due to the significantly higher carbon emission factor of private cars compared to coaches. Despite Village B being in proximity to the Guangzhou-Shenzhen Expressway entrance (only 3 km away), public transportation adoption is limited. Only 2.03% of tourists take public buses, while another 2.03% use a combination of metro and taxis to reach the village. This highlights the inadequacy of public transportation infrastructure, as no direct metro line or dedicated tourist shuttle service is available to the destination. Village B, located in central Guangdong Province, primarily attracts local tourists. Data shows that 91.6% of visitors originate from within Guangdong, with only a small proportion arriving from other provinces. Given the region’ developed economy and high private vehicle ownership in the Pearl River Delta, many tourists prefer flexible self-driving trips, especially considering the relatively short travel distance to Village B (typically one to several hours by car). Additionally, time-sensitive tourists from the Pearl River Delta tend to prioritize the convenience and flexibility of private vehicle use over the limitations of public transportation, where inadequate service frequency and limited coverage further reinforce this preference. As a result, the tourism transportation carbon footprint in Village B is heavily concentrated in private vehicle use, reflecting a structural mismatch between decentralized travel behavior and the lack of an integrated, low-carbon transportation system. To reduce carbon emissions, it is essential to optimize the transportation structure by expanding public transit coverage, promoting carpooling, and encouraging the adoption of more energy-efficient travel modes.

Additionally, homestays in Village B primarily utilize electricity, liquefied petroleum gas (LPG), and firewood as energy sources, with significant variation in energy usage structure across establishments. A limited number of homestays still rely on firewood, which is associated with high carbon emissions. The average carbon footprint per large homestay is significantly higher than that of rural tourism homestays in Songkou Town, while the average carbon footprint per small homestay is relatively comparable. However, the average carbon footprint per small restaurant in Village B is considerably higher than in Songkou Town, primarily due to greater energy consumption from electricity, LPG, and firewood [55]. Since retail products included in the tourism sightseeing carbon footprint model were not considered, particularly the carbon emissions arising from food production for tourism purposes, this omission leads to an underestimation of CO₂ emissions compared to the typical food consumption of local residents [60], and the calculated catering-related carbon footprint of Village B should be regarded as conservative. The carbon footprint associated with sightseeing activities remains low, as there are no large-scale energy-consuming sightseeing facilities, and most tourists explore the village on foot. A small portion of tourists use private cars or coaches within the village; however, their contribution to the total sightseeing carbon footprint is minimal. With respect to shopping-related carbon emissions, specialty stores in Village B primarily rely on electricity, resulting in relatively consistent carbon footprint levels, which remain low overall. The carbon footprint of tourism waste is mainly attributed to incineration, with a much smaller proportion originating from waste transportation. Therefore, strategies such as encouraging low-carbon diets and enhancing recycling rates could effectively mitigate the carbon footprint associated with tourism waste.

To simultaneously advance the “dual carbon” goals and the rural revitalization strategy—and in alignment with the policy directives of Guangdong Province’s “Hundred Counties, Thousand Towns, Ten Thousand Villages High-Quality Development Project” (“Hundred, Thousand and Ten Thousand Project”), the following recommendations are proposed:

(1)

Promote green mobility and reduce dependence on private cars. Establish dedicated electric shuttle bus routes serving scenic areas, and collaborate with shared mobility platforms (such as Meituan and HelloBike) to deploy shared electric bicycles. Optimize the regional transportation network by leveraging rural infrastructure upgrading plans under the “Hundred, Thousand and Ten Thousand Project”, thereby facilitating the connection of Village B with the main urban area of Guangzhou and nearby tourist destinations through low-carbon public transport lines. Simultaneously, we recommend changing the functioning models of rural tourism and improving the spatial organization of tourism infrastructure. This can be achieved by strategically locating key supporting services (such as accommodation and catering) and applying the 15 min city model, so that tourist travel distances are confined to walking range, thereby effectively reducing carbon emissions from transportation [61].

(2)

Innovate waste management strategies and enhance the resource recovery of kitchen waste by introducing small-scale bioconversion systems capable of transforming food waste into organic fertilizers for local agricultural use, thereby reducing emissions from incineration. In addition, a “Low-Carbon Tourism App” could be developed to implement a carbon credit incentive system, rewarding tourists who choose public transport or actively participate in waste sorting. These carbon points could be exchanged for consumption vouchers redeemable within the scenic area. Moreover, a pilot program for low-carbon tourism certification could be established in Village B in collaboration with the Guangdong Provincial Department of Culture and Tourism. This initiative would involve developing “Low-Carbon Rural Tourism Certification Standards” covering key aspects such as energy efficiency, waste management, and environmentally responsible tourist behavior, supported by targeted funding under the “Hundred, Thousand and Ten Thousand Project”.

5. Conclusions

This study conducted a case analysis of the carbon footprint of rural tourism in Guangdong Province, with Village B serving as a representative example. A tourism carbon footprint model for the village was developed based on the life cycle assessment (LCA) method, and quantitative calculations and analysis of the village’s rural tourism carbon footprint were conducted using empirical survey data. The main findings of the study are as follows:

(1)

In 2024, tourism transportation constituted the largest portion of Village B’s rural tourism carbon footprint, accounting for 85.18% of the total. Tourism’s dining and accommodation accounted for 6.93% and 5.10%, respectively, while the combined carbon footprint of waste, touring, shopping, and entertainment was less than 3%. Together, transportation and dining comprised over 90% of the total carbon footprint, making them the primary sources of carbon emissions and key targets for mitigation efforts. The mismatch between the decentralized travel pattern and the limited availability of low-carbon, centralized transportation infrastructure must be addressed to reduce carbon emissions. Optimizing the transportation structure—such as expanding public transport coverage and promoting carpooling—and encouraging tourists to adopt more efficient travel modes are essential. Additionally, transitioning dining services to clean energy is imperative. This includes replacing high-carbon fuels with cleaner alternatives and offering subsidies to restaurants still relying on firewood, encouraging a shift to liquefied petroleum gas (LPG) and the gradual elimination of high-emission fuels.

(2)

In 2024, the total rural tourism carbon footprint of Village B was 7,731.23 tons, with an average per tourist carbon footprint of 38.656 kg/p/a. Compared to existing studies, this per capita value is relatively high, suggesting that the village’s capacity for low-carbon, sustainable development is at a relatively low level. Therefore, to simultaneously advance the “dual carbon” goals and rural revitalization strategy—and in alignment with the policy direction of Guangdong Province’s “Project of High-quality Development in Hundred Counties, Thousands Towns and Ten Thousand Villages” (“Hundred, Thousand and Ten Thousand Project”)—it is essential to enhance low-carbon practices across key sectors. These include strengthening energy transformation in dining services, promoting low-carbon transportation, expanding public transit infrastructure, and improving energy efficiency and emission reduction management in accommodation, catering, and waste treatment facilities.

Overall, this micro-level, data-driven assessment not only provides an empirical foundation for quantifying rural tourism carbon emissions in Guangdong Province but also offers actionable insights that can inform broader decarbonization policies. By demonstrating that transportation-related emissions dominate the carbon footprint, this study underscores the urgency of integrating low-carbon mobility planning into rural revitalization strategies. Moreover, the methodological framework can serve as a replicable model for other rural destinations throughout China, as well as for ecologically sensitive areas elsewhere. At the policy level, embedding carbon footprint assessments into regional tourism development plans, incentivizing clean energy transitions in rural businesses, and promoting environmental awareness among stakeholders will be critical steps to align rural tourism growth with national “dual carbon” goals and international climate commitments such as the Glasgow Declaration.

6. Limitations and Outlook

This study has some limitations. First, the study assumes that tourists use the same mode of transportation for both outbound and return trips. Although this assumption simplifies the calculation process, variations in transportation modes for return trips may lead to deviations in carbon footprint estimates. Due to data limitations, the catering carbon footprint model did not incorporate upstream emissions from the production of food consumed by tourists. Future research could expand the carbon emissions inventory to include supply chain emissions from livestock and other food production activities. Second, data collection was limited to the year 2024, without longitudinal or multi-year tracking. The study also assumes consistency in tourist behavior across peak and off-peak periods, without conducting comparative seasonal analysis. Future research should incorporate seasonal comparisons and conduct cross-year longitudinal studies. Additionally, the methodology could be enhanced by systematically collecting seasonal data across multiple years to enable more robust and comprehensive scenario-based analyses of seasonal variability. Moreover, this study does not comprehensively address carbon management strategies. Although the carbon footprint was quantitatively assessed, the collaborative mechanisms among stakeholders—such as local governments, tourism enterprises, and community organizations—were not systematically examined. Future studies should incorporate policy document analysis and stakeholder interviews to enrich the governance dimension of carbon footprint reductions. Future research could leverage the rural digital platform of Guangdong Province’s “Project of High-quality Development in Hundred Counties, Thousands Towns and Ten Thousand Villages” to build a dynamic carbon footprint monitoring system for Village B. Such a system could facilitate real-time monitoring of emission reduction efforts and serve as a replicable model for promoting low-carbon transitions in rural tourism.