Low-carbon tourism

With the promotion of global carbon reduction, carbon emissions from the tourism industry have attracted attention from countries around the world, and related theories and policies have surged. Hence, increasing research has been conducted on low-carbon tourism as an emerging tourism paradigm. Low-carbon tourism is a sustainable development approach for the tourism industry that utilizes low-carbon technologies and management methods, intending to reduce carbon emissions and improve tourism quality through measures to reduce carbon emissions and pollution from transportation, accommodation, sightseeing, shopping, and entertainment [21]. The development of low-carbon tourism can result in economic, social, and environmental benefits to tourist destinations, cities, and regions, and is a common choice for promoting tourism and economic development [22,23]. There are interrelationships among the economy, society, and environment of tourist destinations. Additionally, sustainable development is a multidimensional concept that includes the economy, society, and environment [24]. Thus, tourist destinations contain many interdependent and highly nonlinear relationships, manifested as dynamic and constantly evolving complex systems [25]. However, research on low-carbon tourism has mainly focused on single aspects such as low-carbon tourism products or activity planning, low-carbon tourism behavior analysis based on tourist perspectives, and evaluation of carbon emissions from tourism [26–29].

Zhang and Zhang [30] examined tourist destination cities and constructed an evaluation index system encompassing three dimensions: low-carbon tourism economy, low-carbon tourism environment, and low-carbon tourism society. Their study highlighted the importance of carbon dioxide emissions and energy consumption in hotels, tourist attractions, and other tourism enterprises, as key economic and environmental indicators. They also emphasized the low-carbon literacy of tourism stakeholders, including residents, tourists, and tourism enterprises. Fakfare and Wattanacharoensil [31] analyzed low-carbon tourist behavior on island destinations by integrating environmental inputs, emotional states, and behavioral responses. They identified key influencing factors, such as service providers, tourism activities, and environmental management. These studies offer theoretical support for the constructing a comprehensive and scientific low-carbon tourism evaluation system based on a system perspective and the characteristics of island destinations in this study.

Sustainable development of island tourism

The sustainable development of tourist destinations involves responsible management that maximizes their potential while balancing important economic, environmental, and socio-cultural factors [32]. Islands have long been popular tourist destinations due to their unique resources. However, the fragile nature of small island environments has led to increased research on the sustainable development of island tourism. Given the relative isolation of islands, it is possible to disentangle the causal effects of island tourism development on their society, environment, and economy and conduct focused research on sustainable island tourism development [33]. Promoting sustainable development in island regions and island tourism requires careful management of stakeholder relationships (among tourism enterprises, tourists, the tourism service industry, residents, and the government) to effectively manage public resources and foster collaboration [34,35]. Therefore, to address the complex challenges of sustainable island tourism, a dynamic tourism system is needed that includes all subsystems (such as economic, social, cultural, ecological, technological and political dimensions). These systems must be evaluated and optimized to establish effective management systems and guiding policies [36–38].

Most of the empirical studies on the evaluation of island tourism development in China have analysed the coupling coordinated development level of the island tourism systems by constructing an evaluation index system for all or two of the three factors, namely, tourism, the socio-economy, and the ecological environment. The results of these studies showed that the level of coupling and coordination between island tourism and the ecological environment [39,40] economic growth [41], and socio-economics [42] in China is not high but is gradually increasing. The tourism system is a dynamic system that is composed of multiple elements, and it requires the organic integration and coordination of various aspects. Constructing this system is an important step in the transition from theoretical research to practical application, and it is an effective way to achieve economic, social, and ecological environmental benefits. It is also a key part of relevant policy formulation and construction planning management [43–45].

Driving force-pressure-state-impact-response framework

Studies on the low-carbon aspects using the driving force-pressure-state-impact-response (DPSIR) model have focused on low-carbon cities and transportation. Integrating the DPSIR model into low-carbon cities and transportation management allows for the incorporation of diverse index dimensions, facilitating the comprehensive consideration of both direct and indirect impacts. This approach helps establish a reliable evaluation index system that enables policymakers to evaluate and monitor environmental trends effectively [46,47]. The DPSIR model combines the strengths of the pressure-state-response (PSR) and driving force-state-response (DSR) models, considering five major factors (economic, social, resource, environmental, and policy). As a result, it offers a comprehensive, systematic, adaptable, and holistic framework. Due to these qualities, the DPSIR model is widely applied in sustainable development and low-carbon research [43,48,49].

Then studies on island systems using the DPSIR model have focused on the two aspects of ecological safety and sustainable development [50,51]. Research on tourism systems based on the DPSIR model has mainly focused on ecological security assessment and sustainable tourism assessment [52,53]. For example, Ruan et al. [54] proposed that constructing an evaluation model can be used to explore the current situation and evaluation index system through quantitative and qualitative analysis, and the DPSIR model comprehensively examines the interrelationships between people and the environment from a systematic perspective. Thus, it fully reflects the interaction between tourists, tourist destinations, and the environment and the continuous feedback mechanism between monitoring indicators.

A review of relevant literature revealed that research on low-carbon tourism on islands has mostly focused on the perspective of tourists, exploring their low-carbon behavior preferences, including low-carbon attractions, low-carbon transportation facilities, environmental protection products, low-carbon projects, low-carbon hotels, low-carbon catering, and shopping activities [31]. However, the development of island tourism and the level of low-carbon development depend on a wide range of stakeholders and local economic, social, cultural, and environmental factors. The research has addressed low-carbon tourism, the transition to low-carbon destinations, and the evaluation of low-carbon competitiveness. Some studies have used the DPSIR model and have accounted for the carbon emissions of the tourism industry or specific localities, developing a low-carbon development evaluation index system to address sustainability challenges [55–57]. Therefore, we plan to apply the DPSIR model to construct an accurate and scientific evaluation index system for low-carbon tourism on islands.

Research gap

This literature review not only examined the theoretical foundations of low-carbon tourism and island tourism, but also addressed the theoretical foundations of the methodology that is used in this study. The existing research on tourism destinations and low-carbon development based on the DPSIR model provides a foundation for constructing the theoretical framework of this study. However, no research has been conducted on low-carbon island tourism development using the evaluation system based on the DPSIR model with island systems as the focus and carbon emissions as a specific index.

Consequently, this study conducted a comprehensive analysis of links between the economy, society, resources, environment, policies, and carbon emissions that were related to island tourism development from a system analysis perspective. Based on the DPSIR analysis model and with carbon emissions from tourism as the core index, this study constructed an index system that reflects the overall development level of low-carbon island tourism. The index included five sub-systems of the low-carbon tourism system, that is, its potential driving forces, direct explicit pressure, core carbon source department state, socio-economic environment impact, and government and enterprise response. The low-carbon tourism development level of Changdao Island was analyzed, which could provide theoretical guidance for building a comprehensive marine ecological civilization pilot zone and formulating low-carbon development policies for Changdao Island.

Research design

First, the carbon emissions of each major tourism department were calculated separately. Secondly, to comprehensively assess the development level of the tourism carbon emissions in Changdao Island, we selected five criterion layers, namely driving, pressure, state, impact, and response, according to the DPSIR model. These criterion layers corresponded to various kinds of small indicators. Then, the basic data of the various indicators in the system were collected from 2016 to 2021, and their characters were judged according to their positive or negative impacts on tourism carbon emissions. Thirdly, according to the characters and data of each year, we obtained the information entropy and weights of these indicators in the total system using the entropy weighting method. To ensure the comprehensiveness of the system, we also obtained the Analytical Hierarchy Process (AHP) weights through the AHP method based on the ratings of several experts for the different carbon emission indicators. Fourth, the entropy weights and AHP weights were combined in equal proportions to obtain the comprehensive weights for the overall carbon emission environmental system. Fifth, while the entropy weights fluctuated based on yearly statistical data, the AHP weights derived from expert scores remained consistent each year. By synthesizing the two, the weights of each indicator were calculated annually, and the scoring value of each criterion layer of the environment system was obtained for the carbon emission impact indicators of the system. These scores represented yearly comprehensive assessments of the entire low-carbon island tourism emission system.

Low carbon tourism evaluation index system construction.

Driver-pressure-state-impact-response model. When compared with the PSR model and the DSP model, the DPSIR model comprehensively covers and integrates the five elements of economy, society, resource, environment, and policy. It can also give a clear, complete, and dynamic reflection of the causal, constraint, and feedback relationships of the five indexes, namely driving forces, pressure, state, impact, and response, by establishing a complete D-P-S-I-R causality chain from the perspective of system analysis. This model was initially used in sociological research, but it has gradually been used in the fields of sustainable development and ecological environment, and now it has been applied to studies to evaluate the development of cities, transportation, and other research objects in the low-carbon field.

Construction of an evaluation system. The tourism ecosystem covers aspects, such as economy, society, environment, and tourism development [39]. Additionally, the low-carbon development assessment system covers indicators, such as carbon emissions and carbon emission intensity [56]. Based on relevant literature, policy documents, and reports, it is proposed that the DPSIR model framework should cover dimensions, such as tourism industry development, local economy, society, resources, ecological environment, carbon emissions status, policy guidance, and corporate practices. Hence, to investigate the relationship between tourism, socio-economic development, and carbon emissions in tourism-based island systems, an evaluation system was constructed that included an objective layer for the overall evaluation of the island tourism low-carbon level, five criterion layers, namely, driving forces, pressure, state, impact, and response, the element layers of all the criterion layers, and an indicators layer for specific measurement (Fig 1).

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Fig 1. Driver-pressure-state-impact-response model for the evaluation of low-carbon tourism development on islands.

https://doi.org/10.1371/journal.pone.0312490.g001

The driving forces drive the development of island tourism and are the potential cause for an increase or reduction in carbon emissions. Furthermore, the driving force is an important input factor for the functioning of the tourism system, and it promotes the development of the tourism industry [54,58]. The economic and social macro environment of tourist destinations provides essential support for the development of the local tourism industry. Moreover, the natural and social cultural resources and the location and climate conditions of tourist destinations serve as key attractions for the development of the tourism industry. Based on existing studies, four driving elements (economy, society, resource, and location) and 21 indexes were preliminarily determined.

The pressure is an explicit index that is directly affected by the development of island tourism under the internal action of the driving forces, and it has an impact on the carbon emissions of the islands. “Pressure” poses a significant threat to the low-carbon development of tourist destinations as various tourism activities can increase resource consumption, environmental pollution, and ecological damage. The primary sources of carbon emissions from tourism include transportation, accommodation, and activities. Therefore, four element layers (island tourism market size, island transportation, island accommodation, and island tourism activity) and 16 indexes were preliminarily determined.

The total carbon emissions and carbon intensity indicators are commonly used to quantitatively analyze the tourism footprint and evaluate the low-carbon development of tourism. This study highlights the importance of carbon dioxide emissions and energy consumption from tourism-related sectors, including transportation, accommodation, and tourism activities, in terms of their impact on the low-carbon economy and environmental indicators. The state is the carbon emission state of island tourism and its sub-sectors of transportation, accommodation, and tourism activity under the internal driving forces and direct pressures, and two element layers (tourism and sub-sectors) and five indexes were preliminarily determined.

The impact involves the well-being of human beings in the socio-economic system of tourist destinations [52]. For island tourism destinations, the development of low-carbon tourism has contributed to increased local tourism revenue and economic conditions. Moreover, the low-carbon development of tourism positively affects the local ecological environment. Therefore, in this study, the impact is the impact on the economic income and ecological environment of the islands due to carbon emissions from island tourism, and two element layers and 14 indexes were preliminarily determined.

Response measures involve the policies and management strategies that are proposed by governments and stakeholders to consciously organize and plan low-carbon tourism to address DPSIR issues [53]. In this context, the government and tourism enterprises are considered the two key entities capable of significantly influencing the development of low-carbon tourism. Therefore, in this study, the response is the response to the economic, social, and ecological impacts on the islands, mainly in terms of the measures that were taken to promote low-carbon island tourism development, and two element layers (government [macro-control side] and enterprises [intermediary side]) and 10 indexes were preliminarily determined.

There were 14 element layers and 86 indexes in total. After the elimination of inappropriate, duplicate, and controversial indexes through expert consultation and statistical analysis and those with missing data based on data query and consultation, a total of 14 element layers and 49 indexes were finally determined in Table 1.

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Table 1. Island low carbon tourism development evaluation index system.

https://doi.org/10.1371/journal.pone.0312490.t001

Determination of Index Weights. After the data from the low-carbon island tourism development evaluation index system were obtained, the raw data of the varying attributes and dimensions were nondimensionalized to obtain standardized and comparable data. The extreme value processing method was used to substitute the positive and negative indexes into Eqs (5) and (6) as follows: (5) (6)

Since the AHP method requires experts to compare the importance of one index with another index for each layer and then assign weights subjectively according to the scale table, it has the disadvantage of being influenced by subjective factors. However, the entropy weight method also has the disadvantage of purely objective weighting. Hence, this study used the Entropy Weight Method-AHP combined with the weighting method to determine the index weights, which reduced the disadvantages of both methods and had the advantages of high rationality, systematicness, and accuracy.

A judgment matrix was constructed for the criterion layers and five indicator layers based on the export scores. As seen in Eqs (7)–(10), the AHP weights of the criterion layers and indicator layers were obtained. Also, a consistency test was performed on the judgment matrix. (7) (8) (9) (10) where is the relative weight of the element, A is the judgment matrix, a_ij is the weight vector, and n is the number of indexes; λ_max is the maximum eigenvalue, CI is the consistency index, RI is the average random consistency index, and CR is the consistency ratio.

Based on the standardized data that were obtained by the nondimensionalization of the raw objective data, the entropy weight method was used to conduct a comprehensive evaluation of multiple indexes and calculate the index variability, information entropy, information entropy redundancy, index weights, and comprehensive score using Eqs (11)–(14), and finally the weight of each index was obtained in Table 2. (11) (12) (13) (14) where P_ij is the weight of i to j, e_j is the information entropy, g_j is the information entropy redundancy, and w_j is the index weight obtained by the entropy weight method; n is the number of indexes and m is the number of years.

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Table 2. Indicator weights.

https://doi.org/10.1371/journal.pone.0312490.t002

Additionally, by substituting the AHP weights and the entropy weight method weights into Eq (15), the comprehensive weight w of the indexes was obtained (Table 2). Using Eq (16), the final score of the low-carbon island tourism development level evaluation was calculated.

(15)(16)

Data source

The raw data that were used in this study to measure the carbon emissions from tourism and to construct the evaluation system were obtained from the Zhong Guo Hai Dao Zhi, Statistical Yearbook of Yantai, Statistical Yearbook of Changdao Island, Changhai County Statistics Bulletin of National Economic and Social Development, Changdao Island Comprehensive Experimental Zone Statistics Bulletin of National Economic and Social Development, and other government websites. The government departments of the Changdao Island Comprehensive Experimental Zone Office, such as the Working Committee and Management Committee, the Transportation and Housing Construction Bureau, the Ministry of Publicity, the Ministry of Culture and Tourism, and the Bureau of Ecology and Environment, provided data support. The accommodation data, such as hotel reviews from the Ctrip website, were collected using the Bazhuayu web crawler. Additionally, some of the data were based on the results of previous studies at home and abroad or obtained from field research.

Carbon emissions from tourism

According to Table 3, from 2016 to 2021, the total carbon emissions from tourism in Changdao Island showed a decreasing trend, which was related to the clean energy input and low-carbon transformation but there was also a close correlation with the downsizing of tourism. From 2016 to 2021, the annual carbon emissions from tourism were 1.3135, 1.5244, 1.4915, 1.4796, 0.8766, and 1.013 Mt, respectively.

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Table 3. Annual tourism carbon emissions by sector.

https://doi.org/10.1371/journal.pone.0312490.t003

Changdao Island is an off-shore island tourism destination and the central transportation mode that is used to reach the scenic spot is waterway transportation. Since 2018, to promote the construction of marine ecological civilization and to realize green travel on Changdao Island, there has been a ban on non-local vehicles from entering the island. Hence, the internal road transportation modes that were available for tourists on the island included leased cars, operating fuel cars, electric cars, buses, sightseeing vehicles, bicycles, and walking. In this study, the carbon emissions of the available internal transportation modes were as follows [35]: 0.07 kg/pkm for ships and ferries, 0.075 kg/pkm for fuel cars, 0.01 kg/pkm for electric cars, 0.018 kg/pkm for buses/sightseeing vehicles, and 0 kg/pkm for bicycles/walking. As the only way for tourists to enter the island, water transport was the main source of carbon emissions from tourism transportation, accounting for 67% -70%. During 2016–2019, with the change in the number of tourists, the tourist turnover for each transportation mode fluctuated slightly, and the carbon emissions from tourism transportation also showed a relatively stable trend. During the COVID-19 outbreak, tourism was drastically affected. Specifically, the number of tourists dropped significantly, the tourist turnover was reduced by about 50%, and the total carbon emissions from tourism transportation were reduced to 0.3541 Mt (2020) and 0.3936 Mt (2021) shown in Table 3.

In 1999, the “fishtainment” tourism model emerged in Changdao Island and Penglai, with fishermen’s families in fishing villages as the main operation entity, and it integrated the experience of fishermen’s folk customs, accommodation, catering, and leisure, with tourism and featured fishermen’s lifestyle and fishing culture. Herein, the unit energy consumption of a star-rated fishtainment homestay was 40 MJ/bed-night [62], the unit energy consumption of star-rated hotels was 130 MJ/bed-night [40], and the conversion coefficient between energy consumption and carbon emissions was set to 43.2 g/MJ [7,63]. With the transition of tourists’ tourism purpose to leisure and vacation, the accommodation and catering features of fishtainment become important, and fishtainment homestays became the major choice for overnight tourists in Changdao Island. Thus, they are also the key source of carbon output from tourist accommodation. During 2016–2018, as the number of overnight tourists grew, carbon emissions rose accordingly. During 2018–2020, the carbon emissions from accommodation continuously decreased. During 2020–2021, the carbon emissions from accommodation increased significantly.

Based on previous studies [64] and the main tourism purpose of the Changdao Island tourists, the tourism activities and their unit energy consumption and carbon emissions were determined as follows: sightseeing (energy consumption: 8.5 MJ/capita; carbon emissions: 113.6 g/capita), leisure vacation (energy consumption: 26.58.5 MJ/capita; carbon emissions: 455.0 g/capita), business trip (energy consumption: 16 MJ/capita; carbon emissions: 214.2 g/capita), and other (energy consumption: 3.5 MJ/capita; carbon emissions: 46.9 g/capita). On this basis, the carbon release from the tourism activities of Changdao Island tourists during 2016–2021 were obtained in Table 3. The total carbon emissions from tourism activities were 0.0912, 0.0996, 0.0984, 0.0950, 0.0676, and 0.0802 Mt, respectively, during 2016–2021.

Results of the evaluation of the low-carbon tourism development on the islands

In this section, according to the established low-carbon island tourism development level evaluation index system, the weight of each layer was obtained in Table 2, and the annual comprehensive score was obtained in Fig 2. Additionally, the annual variation in the low-carbon tourism development level, index weights, and score features were analyzed to explore the development pattern and key factors of low-carbon tourism development.

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Fig 2. Results of the evaluation of low carbon tourism on Changdao Island.

https://doi.org/10.1371/journal.pone.0312490.g002

The “impact” was the most influential element of the island low-carbon tourism valuation with a weighted value of 0.2828. The “impact” indicator expressed the feedback role of the changes in the state of the island due to low-carbon tourism development on the ecological environment and economy. At the “impact” level, the ecological environment value was 0.1507 higher than that of the economic value (0.1321). This explains that the development of low-carbon tourism promoted regional air quality, sewage treatment, and green spaces. Apart from the environmental state, economic incomes, such as local employee earnings, ticket revenues, comprehensive tourism revenues, and total retail sales of social consumer goods, also increased due to low carbon development. Therefore, low-carbon tourism built up an industry chain system, with energy saving and consumption reduction helping tourists to have a better experience, which further enabled island tourism to occupy a larger tourism market.

The “response” also occupied a crucial position with a 0.2088 weighted value. It included the actions of people to prevent or mitigate negative impacts on the island environment. Furthermore, the “response” was divided into government planning and corporate practice. However, the weighted value of government planning was 0.1448, which was twice as high as that of corporate practice. This shows that the government plays a crucial role in low-carbon activities. This is especially true when the government establishes enough standard documents for low-carbon development in the tourism field, and this will increase the value of low-carbon tourism. Moreover, establishing low-carbon tourism demonstration areas, increasing low-carbon publicity, and increasing the number of public bus operations are all efficient methods to increase the value of low-carbon tourism. Additionally, clean energy and energy-efficient equipment usage by corporates also influenced the island tourism evaluation with weighted values of 0.0359 and 0.0281, respectively.

The next elements were “pressure” and “state”. The “pressure” was the direct impact of tourism activities on the island’s natural environment. It was the root cause of environmental problems at a weighting of 0.1917. The “pressure” statement for the Changdao area was calculated based on the scale of island tourism, transportation, accommodation, and tourism activities. These were the reasons for local environmental changes and problems. The “state” weighted value was 0.1699. This reflected the status of the carbon emissions from the tourism industry on Changdao Island. In contrast with the other indicators, all the indicators at the “state” level were negative. This is due to the negative effect of carbon emissions on the regional low-carbon development levels. All the “state” indicators had large weighted values for both the entropy weight and AHP methods. This is because carbon emissions are a key criterion for the island tourism environment.

The weight of the “driving forces” was 0.1469. They were the original deep-seated factors that affected the island’s sustainable development. They usually refer to demographic, social, and economic changes that directly or indirectly affect economic changes. In this study, economic, social, resource, and geographical elements were chosen to identify the strength of the “driving forces”. The geographical location was measured by the number of days with suitable temperatures for tourism. The geographical location’s weighted value was 0.0184, which was smaller than the other indicators that were all larger than 0.04 at the “driving forces” level. The economic weighted value was 0.0450, which shows that economics could be one of the most important “driving forces”.

Based on Table 1, the Entropy Weight and AHP Methods yielded almost identical weighting results. However, the Entropy Weight and AHP Method Weights of the economic indicators at the driving level were different. The Entropy Weight Method yielded a higher weight value than that which was obtained using the AHP method. The Entropy Weight Method obtains data that is based on the value of the data because the regional economic income data is a reflection of the regional development and contributes to the sustainable development of the region. Conversely, the weight of the economic income at the “impact” level that was obtained using the entropy weighting method was lower than that for the AHP method. The regional impact of low-carbon development features prominently in the economic income component, and therefore it received a high score for the AHP Method. In addition, the top five indicators with the highest weights were the sewage treatment rate at the impact level, the number of normative documents related to low carbon development and the rate of clean energy usage in enterprises at the response level, and the transportation carbon emissions and total carbon emissions at the state level. The remaining weighted indicators with high values were mainly from the State and Response levels. Thus, it can be generalized that the government and enterprises’ environmental protection actions have substantial value and significance for low carbon evaluation.

The low-carbon island tourism development evaluation index system comprised the objective layer (O), the criterion layer (C), the element layer (E), and the indicator layer (I). In terms of the criterion layer, the “impact” (C4) contributed the most to the low-carbon tourism development of Changdao Island, followed by the response (C5), pressure (C2), state (C3), and driving forces (C1). In terms of the element layer, the weights of the elements in descending order were as follows: the ecological environment (E12), government planning (E13), economic income (E11), carbon emissions from sub-sectors (E9), and carbon emissions from tourism (E10), and location (E4). Hence, the impact of tourism in Changdao Island on the ecological environment, local economy, and resident income was notable. The government support, planning, and management of low-carbon tourism can play an important role in promoting low-carbon tourism, and reducing the carbon emissions of the three core carbon source departments is the key to low-carbon tourism development. Moreover, the regional economic growth level will also become an important factor that drives the choice of tourists regarding the tourism destination. Then, in terms of the indicator layer, the weights of the elements in descending order were as follows: the sewage treatment rate (I41), rate of clean energy usage (I48), number of normative documents related to low-carbon development (I44), carbon emissions from tourism (I30), and total carbon emissions from tourism transportation (I27). These are in line with the main functions of environmental protection and the development direction of building an international zero-carbon demonstration zone in Changdao Island’s comprehensive marine ecological civilization pilot zone.

Managerial implications

Low-carbon tourism is the basic requirement and correct direction for island tourism development. The development of tourism with a low carbon focus will build a protection network for the green and sustainable development of the island ecosystem and it will provide a driving force for the transformation of the island and fishing villages from a traditional development model to a modern development model. Moreover, it can revitalize local economic development and improve the incomes, living standards, and quality of life of the residents of the islands. Although the low-carbon tourism development level in Changdao Island is gradually increasing, there is still a large potential for improvement. Currently, the local government is actively promoting the construction of Changdao Island International Zero Carbon Island and has proposed the establishment of a zero-carbon tourism destination. Considering the dynamic nature of low-carbon tourism development, there is a pressing need to create a comprehensive and systematic indicator system and effective evaluation methods for ongoing monitoring and evaluation. Therefore, we hope that the evaluation index system for low-carbon tourism development that was established in this study will serve as a theoretical framework for monitoring and evaluating zero-carbon tourism.

In light of the three sub-sectors of tourism, the carbon emissions from tourism in Changdao Island were mainly due to tourism transportation and tourist accommodation. When compared with the carbon emissions from other tourism destinations, the carbon emissions from tourism transportation in Changdao Island made up a lower proportion, which was mainly because tourists can only enter the island by ship, which is a central transportation mode with low unit carbon emissions. Additionally, since the end of 2018, private tourist cars were banned from entering Changdao Island, so tourists can only enter the island by ship and use electric cars, shared bicycles, electric bicycles, public electric buses, taxis, walking, and other available vehicles for internal transportation. This promotes carbon reduction. However, since the implementation of the vehicle policy in 2018, the tourism person-time decreased for the first time in 2019 after a continuous slow increase. The ban on private cars has had certain negative impacts on the number of tourists choosing Changdao Island as a tourism destination and constrains tourism development on Changdao Island. Hence, learning from the experience of mass tourism transport planning in coastal areas, island-hopping routes should be built based on factors such as attractions, tourism facilities, and geographical conditions, and convenient tourism services should be introduced, such as tourist sightseeing buses and self-drive electric car hire and charging services. The carbon emissions from tourist accommodation made up a relatively higher proportion. The accommodations are limited by the high costs of clean energy, and fishtainment homestay operators have a low rate of clean energy usage. In light of this, relevant government departments should further encourage and support the use of clean energy in the tourism accommodation sector by increasing clean energy inputs and providing financial incentives while promoting the special fishtainment homestay construction project on Changdao Island. Furthermore, environmentally friendly advertisements, points rewards, or gifts can increase visitors’ awareness of conserving resources and reducing the use of disposable items.

Limitations and future research directions

The main purpose of this study was to develop a causal effect framework based on the DPSIR model to comprehensively and systematically evaluate the development trends of low-carbon tourism on islands. To ensure the comprehensiveness and accuracy of the evaluation indicators, we constructed the overall framework using relevant data from the Changdao Island Marine Ecological Civilization Comprehensive Experimental Zone. However, since this study involves county-level indicators, the types of indicators may evolve due to changes in the statistical organization and personnel. Thus, the application of this framework for long-term and regular evaluations in the region has certain limitations. Nevertheless, this study can serve as a foundation for establishing a zero-carbon tourism destination on Changdao Island and provide a theoretical basis for long-term and regular statistical indicators. In the future, we also plan to evaluate tourism destinations in China and other islands worldwide, and through repeated adjustments and optimizations, build a more widely applicable evaluation model.

In addition, in light of the global carbon reduction goals and China’s dual carbon goals, this study focused on measuring the carbon emissions of island tourism destinations and evaluating the level of low-carbon tourism development. The aim was to identify key factors that promote zero-carbon initiatives and to assist policymakers and tourism planners in providing targeted policy guidance and project planning. However, this study did not investigate the interrelationships among the subsystems and elements in the DPSIR model. Future research should develop comprehensive evaluation models, such as the DPSIR model, structural equation model, data envelopment analysis, and coupled co-scheduling model, to further investigate the degree of mutual influence, efficiency evaluation, synergistic effects, and trends within these frameworks.