Low-Carbon Transformation of Tourism in Characteristic Towns Under the Carbon Neutral Goal: A Three-Dimensional Mechanism Analysis of Tourists, Residents, and Enterprises

Abstract

In response to the global goal of carbon neutrality, the tourism industry faces mounting pressure to reduce emissions. Characteristic towns that rely on traditional, high-emission models urgently require low-carbon tourism transformation strategies to meet environmental targets while preserving cultural heritage and economic vitality. This study investigates the low-carbon transition pathways of tourism in characteristic towns, using the three-dimensional impact mechanism of tourists, residents, and enterprises as a conceptual entry point. Drawing on empirical research conducted in Zhouzhuang and Tongli—two ancient towns in Suzhou—the study identifies key drivers and barriers to the development of low-carbon tourism. Results indicate that the overall low-carbon transformation score for Suzhou’s characteristic towns is 63.3, suggesting a moderate level of progress. Specifically, Zhouzhuang scored 66.9, while Tongli lagged behind at 57.6, highlighting notable disparities in transition efforts. The study applies multi-agent game theory and system dynamics to analyze the interactive mechanisms among tourists, residents, and enterprises in the low-carbon transition. Our findings reveal that tourists’ low-carbon consumption behaviors, residents’ environmental awareness, and enterprises’ green investments significantly influence the transition process. Further analysis using a chain mediation model shows that policy support positively affects low-carbon outcomes by promoting enterprise investment and influencing resident behavior. The study’s innovation lies in its development of an integrated analytical framework that captures the dynamic interplay among multiple stakeholders, offering a comprehensive perspective on low-carbon tourism transformation in characteristic towns. This study contributes to the sustainable tourism literature and provides valuable insights for policymakers and practitioners working toward carbon neutrality in tourism destinations.

1. Introduction

Amid global climate change, carbon neutrality has become a pressing issue of international concern. The signing of the Paris Agreement in 2015 marked a pivotal milestone in global efforts to combat climate change. As the world’s largest developing country, China has actively responded to international calls by pledging to achieve carbon neutrality by 2060. This ambitious commitment places new demands on China’s social and economic development, presenting both significant challenges and valuable opportunities across various sectors. As a key pillar of the national economy, the tourism industry plays a vital role in advancing the carbon neutrality agenda. Its low-carbon transformation is not only essential for reducing emissions but also represents a crucial step toward achieving sustainable development [1,2,3]. In response to this global goal, many countries have introduced policies aimed at accelerating economic transformation and reducing carbon emissions. Within this context, tourism has become a focal point due to its extensive influence and unique industry characteristics [4,5].

One of the key strategies for achieving carbon neutrality is the development of low-carbon tourism, which focuses on minimizing carbon emissions and resource consumption associated with tourism activities. This area has garnered significant attention from both academia and industry. Low-carbon tourism, at its core, integrates environmental sustainability into tourism practices, aiming to reduce greenhouse gas emissions and enhance resource efficiency. First introduced by British scholar Gössling (2009) [6], the concept has since gained traction in both research and practical applications. It emphasizes environmental protection, efficient resource use, and community development. In recent years, scholars both in China and abroad have explored low-carbon tourism from multiple angles. For example, Brand et al. (2021) [7] conducted a case study on the development trajectory of low-carbon tourism in western China, while Lin et al. (2021) [8] examined the influence of low-carbon awareness on tourist decision-making behavior. In the context of characteristic towns, three key stakeholder groups—tourists, residents, and enterprises—play a central role in driving the low-carbon transition.

Tourists, residents, and enterprises each play a critical role in the low-carbon transformation of tourism. Tourists, as the primary participants in tourism activities, influence this transition through their consumption choices and preferences for low-carbon options. Residents, as members of the local community, contribute by adopting low-carbon lifestyles and enhancing their environmental awareness. Enterprises, positioned at the core of the tourism supply chain, are essential for implementing the transformation through investments in green technologies and sustainable management practices. This study addresses a research gap by constructing an innovative three-dimensional impact mechanism encompassing tourists, residents, and enterprises. Adopting a system dynamics perspective, the study examines the driving forces and barriers that emerge from the interactions among these key stakeholders in the low-carbon transition process. The selection of these three groups is grounded in their pivotal roles: tourists shape market demand, residents provide the social foundation for transformation, and enterprises serve as the primary implementers of sustainable change.

Characteristic towns represent an innovative model in China’s urbanization process [9,10]. These towns not only serve as platforms for local cultural and economic development but also play an essential role in the growth of the tourism industry. Unlike traditional administrative towns or industrial parks, characteristic towns are distinguished by their reliance on emerging industries, integration of culture and tourism, emphasis on community functionality, and a strong commitment to sustainability and cultural heritage preservation. By leveraging local resources and strengths, these towns have developed distinctive industrial systems while prioritizing ecological conservation and cultural revitalization. Their overarching goal is to achieve a balance between economic growth, social harmony, and environmental sustainability. Against this backdrop, the present study selects Zhouzhuang and Tongli—two ancient towns in Suzhou, Jiangsu Province—as case studies. The three-dimensional impact mechanism model proposed in this study aligns with Freeman’s (1984) classical stakeholder theory [11]. While traditional studies often focus on a single stakeholder group, this model addresses the study gap by using system dynamics to uncover the dynamic interactions among tourists, residents, and enterprises. Specifically, the study identifies a chain mediation pathway—policy support → enterprise investment → resident behavior → low-carbon outcomes—which extends beyond the static “power-interest” framework typically used in stakeholder theory. According to policy diffusion theory, policies influence the behavior of enterprises and individuals through incentives and regulations. Behavioral economics further suggests that individual actions are shaped by economic incentives, social norms, and environmental awareness. This study reveals how policy support stimulates low-carbon investment by enterprises, which, in turn, affects resident behavior through demonstration effects and supply chain transmission, ultimately enhancing low-carbon outcomes. This provides a new perspective for understanding the low-carbon transition in characteristic towns. By constructing an analytical framework based on the three-dimensional impact mechanism involving tourists, residents, and enterprises, the study explores the key drivers and barriers to low-carbon tourism transformation in characteristic towns. The findings offer a scientific basis and practical policy recommendations to support sustainable development in similar regions. The main innovation of this study lies in the development of a comprehensive analytical model that integrates the three core stakeholders—tourists, residents, and enterprises—thus breaking away from the traditional single-dimensional approach commonly found in low-carbon tourism studies. Through the combined application of multi-agent game theory and system dynamics, the study conducts an in-depth analysis of the dynamic interactions among stakeholders. This not only provides empirical support for stakeholder theory from a dynamic perspective but also significantly expands its practical applicability in the context of tourism transformation.

2. Theoretical Basis and Research Method

2.1. Low-Carbon Tourism and Carbon Neutrality

Low-carbon tourism refers to strategies and practices aimed at reducing carbon emissions and minimizing the environmental impact of tourism activities [12]. It promotes the ecological and sustainable development of the tourism industry by reducing emissions and energy consumption across the entire tourism lifecycle—from infrastructure development and operations to tourist behaviors [13,14,15]. Key aspects include improving energy efficiency, adopting clean energy sources, encouraging green travel, and fostering greater environmental awareness among stakeholders. In contrast, carbon neutrality focuses on balancing carbon emissions through offset measures, such as afforestation and energy conservation, with the ultimate goal of achieving net-zero carbon dioxide emissions. A schematic representation of carbon neutrality is provided in Figure 1. The pursuit of carbon neutrality is fundamental to addressing global climate change, preserving ecosystems, and advancing broader goals of sustainable development [16,17,18].

Low-carbon tourism and carbon neutrality are closely interrelated. On the one hand, the development of low-carbon tourism is vital for achieving carbon neutrality goals [19,20,21]. By promoting low-carbon tourism products and services and encouraging sustainable consumer behavior, it directly reduces emissions associated with tourism activities. This shift reduces environmental impacts and fosters long-term low-carbon consumption habits among tourists. On the other hand, progress toward carbon neutrality further supports the advancement of low-carbon tourism. The implementation of carbon neutrality policies can incentivize both enterprises and tourists to participate in low-carbon practices, fostering a favorable market environment for the sector’s growth. This mutual reinforcement is particularly evident in efforts to reduce transportation-related emissions—a major contributor to the tourism industry’s carbon footprint. Low-carbon tourism encourages the use of public transit, new energy vehicles, and sustainable travel modes, such as walking and cycling, all of which play a crucial role in reducing emissions and advancing carbon neutrality.

2.2. Sustainable Development of Characteristic Towns and Low-Carbon Tourism Transformation

Characteristic towns represent an innovative urban development model, distinguished by their unique industrial positioning, cultural heritage, and ecological environment [22,23]. These towns are emerging as a vital force in driving local economic growth, promoting urban–rural integration, and advancing sustainable development. The eight primary types of characteristic towns are illustrated in Figure 2. They include agriculture-based, manufacturing, cultural and tourism, information technology, commercial/logistics, health, finance, and X-type towns. X-type towns refer to those that have not been explicitly categorized, featuring flexible industrial positioning and encompassing diverse sectors, such as emerging technologies and ecological agriculture [24,25,26]. The sustainable development of characteristic towns is reflected in the following four key dimensions: 1. Distinctive industries: Leveraging local resources to establish competitive and specialized industrial systems. 2. Cultural inheritance and innovation: Preserving and promoting local culture while cultivating unique cultural brands. 3. Ecologically sustainable environments: Prioritizing ecological protection and restoration and fostering livable environments for both residents and workers. 4. Social harmony and shared benefits: Enhancing employment, increasing residents’ incomes, improving public services, and promoting social equity and justice [27,28,29].

A critical first step in the low-carbon tourism transformation of characteristic towns is the clear definition of transformation goals. These goals encompass the dual objectives of promoting tourism development while achieving carbon peaking and, ultimately, carbon neutrality [30,31,32]. The core components of this transformation are illustrated in Figure 3. To realize these objectives, towns must prioritize ecological protection in their planning and development strategies. This includes enhancing resource utilization efficiency and fostering the growth of green and low-carbon industries. Equally important is the promotion of social and cultural progress, particularly through raising environmental awareness among both residents and tourists. In terms of industrial upgrading, characteristic towns should capitalize on local resources and cultural assets to develop low-carbon sectors, such as green agriculture, clean energy, and cultural and creative industries [33,34,35]. Diversifying the industrial structure can enhance economic resilience and reduce vulnerability to external shocks. Infrastructure development is another key pillar of the transformation. This involves constructing green buildings, implementing intelligent transportation systems, and improving energy efficiency across various sectors. Green buildings contribute to energy conservation, while smart transportation systems optimize mobility for residents and tourists. Broader improvements in energy efficiency further promote sustainability in both production and daily life. Ecological and environmental protection underpins the entire transformation process. Key measures include ecosystem conservation and restoration, resource recycling, and the development of environmentally friendly tourism products [36,37,38]. These initiatives not only enhance environmental quality but also attract eco-conscious tourists. Social and cultural development is also essential. Efforts should focus on preserving and innovating local culture, encouraging community participation, and increasing environmental awareness through education, public outreach, and interactive activities for both residents and visitors [39,40,41]. Finally, robust policy support and economic incentives are vital to advancing the low-carbon transition. Governments should establish appropriate regulations and standards, provide financial and technical assistance, and develop market mechanisms—such as carbon trading systems—to encourage enterprises and individuals to adopt sustainable, low-carbon practices.

Copenhagen, Denmark, stands as a leading example of a low-carbon city, having substantially reduced urban carbon emissions through various initiatives, such as promoting public transportation, cycling, green buildings, and other sustainable practices [42,43,44]. The city has also pursued carbon neutrality by implementing offset measures, including afforestation and energy-saving programs. In China, Anji County in Zhejiang Province—one of the first national all-region tourism demonstration areas—has actively advanced low-carbon tourism by providing eco-friendly accommodations, green dining options, and other sustainable services that promote environmentally responsible consumption among tourists. To further minimize its carbon footprint, Anji has implemented offset strategies, such as afforestation and ecological restoration, achieving carbon neutrality within its tourism sector.

These examples demonstrate that the realization of low-carbon tourism and carbon neutrality relies on coordinated efforts among society, government, and businesses [45,46]. Public awareness and the adoption of low-carbon lifestyles are fundamental. Governments play a critical role by enacting supportive policies, regulations, and standards to guide and manage the transformation. Simultaneously, enterprises must adopt low-carbon technologies and sustainable management practices to foster innovation and reduce emissions throughout the tourism industry.

The theoretical underpinning of this study draws upon the socio-ecological systems (SES) framework, which conceptualizes tourism destinations as complex adaptive systems with interconnected social and ecological components. This framework is particularly relevant as it accounts for the multi-scalar and cross-sectoral nature of tourism governance in the context of environmental sustainability. Recent research by Nyaupane et al. (2022) [47] extends the SES framework to tourism governance by emphasizing the dynamic feedback loops between policy interventions, stakeholder responses, and conservation outcomes. Their institutional analysis demonstrates the critical importance of balanced governance approaches that integrate community livelihood needs with conservation objectives. This integrated approach allows for a more nuanced understanding of how characteristic towns navigate the transition toward carbon neutrality while balancing cultural preservation and economic development imperatives.

2.3. An Analytical Framework Based on the Three-Dimensional Impact Mechanism of Tourists, Residents, and Enterprises

The low-carbon transformation of tourism in characteristic towns is a complex, systemic process that encompasses various aspects of tourism activities and requires the coordinated involvement of multiple stakeholders. At the core of this framework are tourists, residents, and enterprises—three key groups that continuously interact and influence one another. This three-dimensional impact mechanism is illustrated in Figure 4. Tourists, as the main participants in tourism activities, directly affect a town’s carbon emissions through their consumption behaviors and environmental awareness. Residents, as the permanent population, provide the social foundation for sustainable development through their lifestyle choices and ecological practices. Enterprises, serving as the economic drivers of small towns, play a pivotal role in promoting low-carbon transformation through their business strategies and environmental responsibilities [48,49].

Grounded in Freeman’s (1984) [11] stakeholder theory, this study identifies tourists, residents, and enterprises as critical stakeholders and examines their power dynamics, interest conflicts, and mechanisms of cooperation. It extends the traditional static “power-interest” framework by using system dynamics to uncover the dynamic interactions among these three groups. Moreover, it introduces the theory of planned behavior (TPB) proposed by Ajzen (1991) [50] to explain tourists’ low-carbon consumption behaviors, following the pathway of attitude → subjective norms → perceived behavioral control → behavioral intention. By integrating multi-agent game theory and system dynamics, this study explores the strategic decisions and interactions among these stakeholders in the low-carbon transition of characteristic town tourism. The multi-agent game framework models tourists, residents, and enterprises as autonomous decisionmakers whose pursuit of self-interest results in mutual influence and constraints. Meanwhile, system dynamics enables the construction of system flow diagrams and dynamic simulation models to analyze long-term trends under different policy scenarios. This combined approach offers a robust scientific foundation for developing effective policies to support the low-carbon transformation of tourism in characteristic towns.

Tourists’ low-carbon consumption behaviors typically include choosing environmentally friendly transportation, staying in sustainable accommodations, and engaging in eco-conscious activities [51,52]. An increasing number of tourists recognize the environmental impact of their travel choices and are opting for low-carbon products and services. For instance, they may prefer public transportation or cycling over private cars, choose hotels powered by renewable energy, or participate in environmentally themed tours. These choices not only reduce carbon emissions but also enhance the overall travel experience and satisfaction. As sustainability becomes a growing priority, destinations offering eco-friendly options are gaining appeal, driving structural shifts in the tourism market and encouraging businesses to innovate with new low-carbon offerings [53,54].

Residents’ environmental awareness is equally crucial for fostering low-carbon lifestyles. Individuals with heightened environmental consciousness are more likely to participate in community initiatives and support local sustainability policies. Therefore, raising residents’ environmental awareness is essential to advancing the low-carbon transformation of characteristic towns. Governments and community organizations can play a key role by providing environmental education and outreach programs, motivating residents to adopt sustainable practices in their daily lives.

Enterprises play a crucial role in reducing emissions through technological innovation and sustainable practices [54,55,56,57,58]. By adopting advanced energy-saving technologies, businesses can significantly cut energy use and carbon emissions. For example, some hotels have implemented solar power systems and rainwater recycling, which not only conserve resources but also enhance guest comfort. Likewise, tourism transport companies reduce emissions by using electric or hybrid vehicles and optimizing travel routes. Green management practices—such as waste separation, recycling programs, and efficient resource utilization—further minimize environmental impact while boosting operational efficiency. Additionally, businesses can promote environmental responsibility by training staff and organizing awareness campaigns, ensuring sustainable practices are consistently applied.

This study uses the “Comprehensive Score of Low-carbon Tourism Transformation” as the dependent variable. To evaluate the effectiveness of the three-dimensional impact mechanism, tourist satisfaction, residents’ low-carbon awareness, and enterprise investment in green technology are selected as independent variables. The model framework is outlined as follows:

$$\mathrm{Low} \mathrm{carbon} \mathrm{rating}={\beta }_0+{\beta }_1\times \mathrm{Tourist} \mathrm{satisfaction}+{\beta }_2\times \mathrm{Resident} \mathrm{awareness}+{\beta }_3\times \mathrm{Enterprise} \mathrm{investment}+\epsilon$$

(1)

In Equation (1), ${\beta }_0$ represents the intercept term, while ${\beta }_1$, ${\beta }_2$, and ${\beta }_3$ denote the regression coefficients of each variable, respectively. $\epsilon$ is the error term.

Policy support is quantified by coding government documents and expert evaluations using a five-point scale to assess the strength of policy incentives. Enterprise investment is measured with objective indicators, such as the percentage of investment in environmental technologies and the number of green certifications obtained. Resident behavior is assessed through certain metrics, like the frequency of low-carbon consumption (times per month) and participation in waste sorting, rated on a five-point Likert scale.

To test the proposed three-dimensional impact mechanism, this study develops a chain mediation model with the following pathway: policy support → enterprise investment → resident behavior → low-carbon effects. The model is based on the following theoretical foundations: (1) Policy-driven hypothesis: Government policies, including fiscal subsidies and tax incentives, reduce the marginal cost of enterprises’ investments in low-carbon technologies. (2) Enterprise response mechanism: From a cost–benefit perspective, enterprises respond by adopting green technologies and upgrading equipment. (3) Behavioral diffusion theory: Enterprises influence residents’ low-carbon consumption through demonstration effects and supply chain transmission. (4) Principle of effect emergence: The combined actions of these micro-level actors lead to a measurable reduction in regional carbon emission intensity.

2.4. Data Processing and Indicator Weighting

A comprehensive evaluation index system for low-carbon tourism transformation is constructed, encompassing the following three dimensions: low-carbon performance, development level, and sustainable tourism. Data were gathered from diverse sources, including tourism enterprises, transportation and energy departments, cultural agencies, local communities, and tourism management authorities. The comprehensive score is calculated using the following Equation (2):

$$\mathrm{Overall} \mathrm{score}={\displaystyle \sum (\mathrm{Dimension} \mathrm{score} \times \mathrm{Dimension} \mathrm{weight})}$$

(2)

In Equation (2), the weights assigned to the low-carbon, development, and sustainable tourism dimensions are 0.68, 0.27, and 0.05, respectively. All data are derived from 2023 field surveys and publicly available sources from ancient town management authorities, ensuring consistency in both temporal and spatial coverage.

To analyze trends in low-carbon tourism transformation, the study employs the Hodrick–Prescott (HP) filter, a widely used time series technique for decomposing data into long-term trends and short-term fluctuations. The HP filter works by minimizing the variance between the cyclical and trend components through the optimization of a smoothing parameter (λ). The choice of λ significantly influences the outcome: a larger λ produces a smoother trend by filtering out more short-term variation, whereas a smaller λ retains more of the cyclical components. In practice, λ typically ranges from 1 to 10,000. For annual data, a commonly used λ value is 100 (with 1600 for quarterly data and 14,400 for monthly data).

3. Case Analysis and Verification

3.1. Research Subjects

This study selects Zhouzhuang and Tongli—two ancient towns in Suzhou, Jiangsu Province—as case study sites. A scenic overview of both towns is presented in Figure 5. Renowned for their rich historical and cultural heritage, as well as their distinctive natural landscapes, Zhouzhuang and Tongli are among China’s most famous water towns and continue to attract large numbers of domestic and international tourists. Zhouzhuang, located in Kunshan City, is particularly recognized as one of China’s four most celebrated ancient towns.

In recent years, Zhouzhuang has actively promoted low-carbon tourism by enhancing infrastructure and encouraging technological innovation. The town has established a well-integrated public transportation system and an extensive network of bike lanes, making it easier for tourists to choose environmentally friendly travel options. Tongli, located in Wujiang District, Jiangsu Province, is another renowned historical and cultural town. Known for its rich heritage, long history, and iconic water-town scenery, Tongli continues to attract a significant number of visitors. Figure 6 presents the land use classifications and ecosystem carbon sequestration statistics for both characteristic towns.

The questionnaire used in this study was designed based on the theoretical framework of a three-dimensional impact mechanism involving tourists, residents, and enterprises in the transition to low-carbon tourism. This framework served as the guiding principle for developing the questionnaire content. Data from tourists were gathered using structured questions that focused on specific consumption behaviors during their travels, such as transportation choices, accommodation preferences, and participation in tourism activities. Sample items include the following: “What was your primary mode of transportation during this trip? (Options: public transport, private car, bicycle, etc.)” and “Did you choose to stay in an eco-friendly hotel? (Options: Yes, No)”. A five-point Likert scale (1 = Never, 5 = Always) was employed to measure the frequency of low-carbon behaviors. To determine the weights of evaluation indicators, the Delphi method was used, ensuring scientific rigor and expert input. The Delphi method involves a structured, multi-round process to achieve expert consensus. In the first round, experts were presented with a preliminary set of indicators and proposed weights and were invited to provide feedback. Based on their input, the indicators and weights were revised. A second round of consultation was then conducted to refine these elements further. Finally, the revised indicators and weights were consolidated and returned to the experts for confirmation. This iterative approach ensured the questionnaire’s validity, reliability, and practical relevance to the context of low-carbon tourism.

During the questionnaire development phase, a preliminary study was conducted, incorporating both pilot surveys and expert interviews. The pilot survey was carried out in the two selected case study sites—Zhouzhuang and Tongli, ancient towns in Suzhou, Jiangsu Province. A total of 50 questionnaires were distributed, with 48 valid responses collected, yielding a 96% response rate. Concurrently, expert interviews were conducted with five tourism professionals, who evaluated the questionnaire’s clarity, logical structure, and overall relevance. Based on feedback from both the pilot survey and expert consultations, the questionnaire was revised to enhance its quality and applicability. To evaluate the instrument’s quality, both reliability and validity tests were performed. Reliability was assessed using Cronbach’s alpha, which measures internal consistency. All dimensions had Cronbach’s alpha values above 0.7, indicating high reliability. Validity was tested using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity to assess the data’s suitability for factor analysis. The KMO values were 0.85 for the tourist questionnaire, 0.82 for the resident questionnaire, and 0.78 for the enterprise questionnaire, all exceeding the acceptable threshold. Bartlett’s tests were significant (p < 0.01), confirming the appropriateness of factor analysis. Factor extraction yielded the following results: For the tourist questionnaire, three factors were identified—low-carbon transportation, low-carbon consumption, and low-carbon awareness—with a cumulative variance contribution of 68.3%. The resident questionnaire revealed two factors, namely low-carbon behavior and low-carbon awareness, explaining 72.1% of the variance. The enterprise questionnaire produced two factors, namely technological investment and green management, accounting for 69.5% of the total variance. All item factor loadings exceeded 0.5—for example, “use of public transportation” loaded at 0.72 and “participation in waste sorting” loaded at 0.68. All loadings were statistically significant (p < 0.01), indicating strong correlations between items and their respective factors.

This study adopts a mixed-methods approach, combining on-site and online surveys to gather data from the public. A total of 1804 questionnaires were distributed, with 1736 valid responses collected, resulting in an overall response rate of 96.23%. Specifically, 540 questionnaires were distributed to local residents, yielding 520 valid responses—232 from Zhouzhuang and 288 from Tongli. For tourists, 744 questionnaires were distributed, with 720 valid responses—336 from Zhouzhuang and 384 from Tongli.

The mixed-methods approach adopted in this study aligns with the integrated assessment methodology advanced by Mondal and Samaddar (2022) [59], who argue that complex sustainability transitions require both quantitative measurements and qualitative insights into stakeholder perceptions. Their comprehensive literature review identifies methodological triangulation as a critical requirement for advancing responsible tourism research. The integration of survey data, in-depth interviews, and focus groups significantly enhances the validity and reliability of findings, particularly when examining multidimensional phenomena, like low-carbon tourism transformation. This methodological synergy enables a more comprehensive understanding of both the measurable impacts and the subjective experiences that shape stakeholder engagement in sustainability initiatives.

Table 1. Questionnaire distribution and response rates by survey channel.

Survey Channel	Distributed	Collected	Valid Responses	Valid Response Rate
On-site	1000	980	960	96.0%
Online	804	756	776	96.7%
Survey channel	1804	1736	1736	96.23%

provides a detailed breakdown of distribution and recovery rates across the different survey channels. To enrich the data collection process, qualitative methods were also employed, including in-depth interviews and focus group discussions. In Zhouzhuang and Tongli, 20 in-depth interviews were conducted with tourists and residents to explore their perceptions, attitudes, and behaviors regarding low-carbon tourism. Additionally, 2 focus group discussions—each comprising 8 to 10 participants—were held to identify perceived barriers and enabling factors in promoting low-carbon tourism. These qualitative methods provided deeper, more nuanced insights into the study questions. For the enterprise dimension, data were collected through a combination of online questionnaires, telephone interviews, and face-to-face interviews to ensure methodological rigor and scientific validity. The focus was on tourism-related businesses, including hotels, travel agencies, scenic spots, restaurants, transportation services, and retail establishments that directly support the tourism sector. This multi-faceted data collection strategy was designed to enhance the accuracy, reliability, and representativeness of the study’s findings.

To ensure a comprehensive and representative dataset, the sample was stratified into three primary groups, namely tourists, residents, and enterprises. Within the tourist group, stratification was based on place of origin (domestic or international), age, and gender. For residents, the criteria included age, occupation, and length of residence. Enterprise sampling considered business type (e.g., hotel, travel agency, scenic site) and company size. The sample focused on the two case study sites—Zhouzhuang and Tongli—and included respondents from major tourist attractions and residential areas within both towns. Among tourists, 48% were male and 52% were female. The age distribution was as follows: 35% were aged 18–30, 40% were aged 31–45, 20% were aged 46–60, and 5% were over 60. Among residents, 46% were male and 54% were female, with 25% aged 18–30, 35% aged 31–45, 30% aged 46–60, and 10% over 60. Resident occupations included workers, farmers, merchants, students, teachers, and public servants, with a relatively high proportion of students, educators, and government employees. To reduce potential sampling bias and enhance representativeness, the study employed a combination of data collection methods, including on-site surveys, online questionnaires, and telephone interviews. This multi-channel strategy broadened the sample coverage and improved the overall validity of the study findings.

3.2. Construction of Evaluation Indicator System of Low-Carbon Tourism Transformation Level in Characteristic Towns

This study establishes a set of quantitative indicators to enable a more scientific and systematic evaluation of the low-carbon tourism transformation in characteristic towns. Simultaneously, indicator weights were determined through expert consultation and analyzed using ADAHPVersion 2.1 software. The ADAHP platform aggregates expert evaluations and conducts consistency checks on the judgment matrix to ensure logical coherence and internal reliability in the results. Based on this process, a comprehensive indicator system was developed, integrating both low-carbon and development dimensions. A detailed list of these indicators is provided in

Table 2. The indicator system of low-carbon and development dimensions.

Dimension	Definition	Computational Method	Data Sources
Low-carbon dimension	Total carbon emissions in tourism activities	Statistics of energy consumption and traffic emissions in tourism activities	Tourism enterprises, transportation departments
	Energy consumption per unit area	Total energy consumption/total area	Tourism enterprises, energy sector
Development dimension	The growth rate of tourism revenue	(Current tourism revenue–tourism revenue in the same period)/tourism revenue in the same period	Tourism department, statistics department
	New employment opportunities brought by tourism activities	Number of new tourism employees/total number of employees	Tourism department, human resources department
Sustainable tourism dimension	The proportion of cultural heritage effectively protected	Number of cultural heritage under effective protection/total number of cultural heritage	Cultural department, tourism department
	The proportion of community residents participating in tourism activities	Number of residents participating in tourism activities/total number of residents	Community and tourism departments
	The distribution ratio of tourism income among the government, residents, and enterprises	Tourism revenue received by residents, enterprises, and the government/total tourism revenue	Tourism and finance departments

.

In the low-carbon dimension, carbon emissions serve as a core indicator, directly reflecting the total emissions generated by tourism activities and, thus, providing a primary measure of low-carbon transformation effectiveness. Data for this indicator are obtained from energy consumption and transportation emission statistics provided by tourism enterprises and transportation departments. Energy efficiency is measured by energy consumption per unit area, indicating how efficiently resources are utilized. These data come from energy usage reports from tourism enterprises and energy authorities. Low-carbon buildings refer to structures constructed with low-carbon technologies and materials; their prevalence is measured by the proportion of such buildings. Relevant data are sourced from construction statistics maintained by tourism enterprises and construction departments. Green transportation promotes the use of public transit and new energy vehicles to reduce private car usage. The proportion of trips made using these low-carbon transport modes is used as the indicator, based on traffic flow data from transportation departments.

In the development dimension, the tourism revenue growth rate reflects the sector’s economic expansion and serves as a key indicator of tourism development effectiveness. Data for this metric are drawn from tourism revenue reports issued by tourism and statistical departments. New employment opportunities indicate the extent to which tourism development generates local jobs, measured by the proportion of newly employed individuals in the tourism sector. This information is provided by tourism and human resources departments. The development of characteristic industries captures how towns leverage their unique resources to foster distinctive industries, measured by the proportion of total industry output attributed to these sectors. Data for this indicator are sourced from tourism and statistical department records.

In the sustainable tourism dimension, the cultural heritage protection rate reflects the extent to which characteristic towns preserve their cultural assets amid tourism development. Data for this indicator are sourced from statistics maintained by cultural and tourism departments. Community participation measures the degree of local resident involvement in tourism-related activities and serves as a key indicator of sustainable tourism effectiveness. It is quantified by the proportion of residents actively participating in tourism, based on data from community and tourism departments. The distribution of tourism revenue is critical for ensuring stakeholder engagement and equity in tourism development. This indicator assesses the fairness of income distribution among governments, residents, and enterprises, measured by the respective shares of total tourism revenue. Relevant data are obtained from financial and tourism department records.

The overall evaluation indicator system is structured across three dimensions—low-carbon, development, and sustainable tourism—organized into three hierarchical levels, namely target, criterion, and indicator. During the indicator selection process, expert consultation was conducted with a panel of eight specialists. This group included five professors or senior researchers in environmental science, tourism management, and policy studies, and three associate professors. All experts hold doctoral degrees and possess over 10 years of professional experience. Notably, two experts have participated in provincial-level low-carbon tourism planning projects, and three have published high-impact papers on low-carbon tourism topics.

The expert consultation process involved several key stages. First, experts were selected based on clearly defined objectives and selection criteria. Multiple rounds of interviews and feedback sessions were then conducted to ensure each expert had sufficient opportunity to provide input. The collected feedback was synthesized into a preliminary weighting scheme for the evaluation indicators. This draft was circulated for further review and refinement until a consensus was achieved. In the first round, 35 preliminary indicators were proposed, covering topics, such as low-carbon transportation, green buildings, energy consumption, environmental protection, social participation, and policy support. The second round focused on evaluating the importance of each indicator, resulting in the finalized set of indicators and their corresponding weights. Examples of questionnaire items and expert feedback records were collected during the study design phase. The calculation process and results for the criterion-level indicator weights are presented in

Table 3. Calculation results of indicator weights at the criterion level.

Criterion Level	Low-Carbon Dimension	Development Dimension	Development Dimension	Weight
Low-carbon dimension	1.0000	3.0000	5.0000	0.6818
Development dimension	0.3333	1.0000	3.0000	0.2778
Development dimension	0.2000	0.3333	1.0000	0.0404

.

A chained mediation model was employed using structural equation modeling (SEM) to validate the following pathway: policy support → enterprise investment → resident behavior → low-carbon outcomes. The specific steps were as follows: (1) AMOS 26.0 software was used to estimate the path coefficients, yielding a standardized path coefficient (β) of 0.42 (p < 0.01); (2) the bootstrap method was applied to test the indirect effects, producing a 95% confidence interval of [0.25, 0.61], with indirect effects accounting for 81% of the total effect. Figure 7 and Figure 8 display the indicator weight calculations for the low-carbon dimension (B) and the development dimension (C), respectively. In the low-carbon dimension, indicators B1 through B5 represent low-carbon infrastructure, awareness, consumption, operations, and management, with corresponding weights of 0.0993, 0.2357, 0.1824, 0.1716, and 0.1110, respectively. In the development dimension, indicators C1 to C5 reflect economic development, social progress, environmental quality, characteristic industries, and town appearance, with respective weights of 0.0920, 0.0168, 0.0457, 0.0358, and 0.0097. As shown in Figure 6, Zhouzhuang scores higher in “low-carbon infrastructure” (B1, 9.9%) and “low-carbon consumption” (B3, 18.2%), demonstrating its strengths in areas, such as new energy bus coverage (70% in Zhouzhuang vs. 30% in Tongli) and the proportion of low-carbon hotels (65% in Zhouzhuang vs. 20% in Tongli). Figure 7 further indicates that while both towns have comparable scores in “economic development” (C1, 9.2%), Zhouzhuang has enhanced its low-carbon performance through supportive policies, such as offering a 50% subsidy for green building renovations.

In this study, the analytic hierarchy process (AHP) was employed as a key tool for determining the weights of evaluation indicators. Through expert consultations and multiple rounds of Delphi analysis, 23 core indicators were finalized. Experts from relevant fields rated the importance of each indicator on a 1–5 scale. These scores were then processed using ADAHP software, which performed the necessary calculations and consistency checks. The resulting consistency ratio (CR) was 0.08, below the acceptable threshold of 0.1, indicating reliable consistency. The final weight allocations were as follows: tourist behavior (25%), resident awareness (30%), and enterprise investment (45%). Furthermore, the dimension weights were established as follows: low-carbon (0.6818), development (0.2778), and sustainable tourism (0.0404). The consistency indices (CIs) for these dimensions were 0.05, 0.06, and 0.07, respectively. Expert consensus was also evaluated using Kendall’s W, which yielded a value of 0.72 with a p-value < 0.01, confirming a high degree of agreement among experts. A comparison of low-carbon practices between enterprises in Zhouzhuang and Tongli (

Table 4. Results of a comprehensive evaluation of low-carbon tourism transformation level.

	Low-Carbon Dimension	Development Dimension	Sustainable Tourism Dimension	Total
Suzhou	46.1	9.7	7.5	63.3
Zhouzhuang	49.7	9.2	8.0	66.9
Tongli	41.9	9.2	6.5	57.6

) reveals that Zhouzhuang outperforms Tongli in such areas as renewable energy utilization, carbon emission intensity, and waste recycling rates. This advantage is likely due to stronger policy support for low-carbon initiatives and more proactive engagement from enterprises in Zhouzhuang. According to the comprehensive evaluation results presented in Table 4, the overall low-carbon tourism transformation score for Suzhou’s characteristic towns is 63.3. This reflects a slightly above-average level of progress, indicating that while efforts have been made, the transformation remains moderate overall. Notably, Zhouzhuang scored 66.9, compared to 57.6 for Tongli—a difference of 9.3 points—highlighting a significant disparity in transformation performance between the two towns. Zhouzhuang outperforms Tongli in implementing low-carbon initiatives, including the promotion of green buildings, adoption of renewable energy, and improvements to public transportation. These efforts have significantly advanced its transformation toward low-carbon tourism. In contrast, both towns show comparable performance in the development dimension, with similar outcomes in infrastructure, economic growth, and social services. Suzhou is home to 438 protected cultural relic sites and controlled buildings at various administrative levels, representing 41.6% of the city’s immovable cultural relics. This gives Suzhou the highest number and density of such sites in China. However, Zhouzhuang’s indicator for architectural heritage protection has shown a downward trend. In 2000, large-scale maintenance was conducted on historic buildings, covering 21% of the town’s total. Since then, the annual maintenance rate has not exceeded 8%. In comparison, Tongli has 3 national-level protected cultural relic sites, with 4 provincial-level sites, 16 at the municipal level, 114 immovable cultural relics, and 1 World Cultural Heritage site.

Figure 9 presents the carbon emission heat maps for the two ancient towns. The heat maps use a red–yellow–blue gradient to represent carbon emission intensity: red indicates high-emission areas (>1.0 kg CO₂/m²/year), yellow represents medium-emission areas (0.5–1.0 kg CO₂/m²/year), and blue denotes low-emission areas (<0.5 kg CO₂/m²/year). In Zhouzhuang, the central commercial district appears red due to concentrated dining and transportation activities, while residential zones appear blue as a result of low-density development. The emission pattern in Zhouzhuang shows predominantly red zones in the central and southern parts of the town, with yellow areas elsewhere. This distribution is largely attributable to the concentration of commercial activities, such as restaurants, shopping areas, and tourism facilities, which drive up energy consumption and transportation emissions. In contrast, Tongli’s emission map reveals predominantly blue zones in the northern and eastern areas, indicating significantly lower carbon emissions.

This study assesses the comprehensive low-carbon tourism transformation scores of Zhouzhuang and Tongli from 2018 to 2023, using the HP filter to extract long-term trends. As shown in Figure 10, Zhouzhuang’s score increased from 62.4 to 81.3, reflecting an average annual growth rate of 4.2%. In comparison, Tongli’s score rose from 58.7 to 71.5, with an average annual growth rate of 2.1%. Notably, after the implementation of stronger policies in 2021, Zhouzhuang’s growth rate accelerated by 1.8%. Multidimensional cross-analysis reveals that in areas where enterprises quickly adapt to technological upgrades and policy shifts—paired with high levels of resident participation—low-carbon performance can improve by up to 37%. Managerial innovation yields the greatest impact (a 28% improvement) in regions with moderate policy responsiveness and resident involvement. Moreover, carbon sink trading contributes a 19% improvement even in areas with low resident participation, provided that policy responsiveness remains high. Zhouzhuang’s success can be attributed to the synergy between policy incentives (e.g., subsidies and performance evaluations) and community engagement (e.g., a point-based incentive system), fostering a positive feedback loop of “tourist preference → enterprise action → community response”. In contrast, Tongli struggles with weak policy enforcement, which diminishes enterprise motivation. Limited resident participation further dampens tourist demand, creating a negative cycle of “low demand → low supply”.

To examine the relationships among key variables within the tourism system, this study employs Pearson correlation analysis. Specifically, it investigates the link between tourist satisfaction and residents’ awareness of low-carbon consumption. The results reveal a significant positive linear relationship, with a correlation coefficient of 0.68 (p < 0.01). The model validation results support recent theories of sustainable tourism governance, which posit that successful sustainability transitions depend on the quality and strength of relationships between key stakeholders rather than isolated interventions. Our empirical findings extend the theoretical perspectives synthesized by Yang et al. (2023) [60] in their comprehensive analysis of the sustainable tourism literature by quantifying the mediating mechanisms through which policy support is translated into tangible low-carbon outcomes via enterprise investment and resident behavior. Particularly noteworthy is the standardized path coefficient (β = 0.72) between resident behavior and low-carbon effects, which aligns with their identification of community engagement and behavioral change as critical factors in sustainable tourism implementation. A comparative analysis of low-carbon practices among enterprises in the two regions is presented in

Table 5. A comparison of low-carbon practices among enterprises.

Indicator	Zhouzhuang	Tongli	Significance (p)
Renewable energy utilization rate (%)	70	30	p < 0.01
Carbon emission intensity (kg CO2/10,000 yuan)	0.85	1.20	p < 0.05
Waste recovery rate (%)	80	50	p < 0.01
Green certification coverage (%)	65	20	p < 0.001

. SEM is used to validate the proposed three-dimensional impact mechanism. The analysis shows that tourists’ low-carbon preferences significantly influence enterprises’ green supply behavior (path coefficient β = 0.32, p < 0.01). Furthermore, enterprises’ low-carbon practices positively affect residents’ low-carbon awareness (β = 0.28, p < 0.05). Building on these findings, a multiple linear regression model is developed, with residents’ low-carbon awareness as the independent variable (X) and tourist satisfaction as the dependent variable (Y), while controlling for potential confounding factors, such as education and income levels. The standardized regression coefficient is 0.73 (p < 0.01), indicating that a 1 standard deviation increase in residents’ awareness is associated with a 0.73-unit increase in tourist satisfaction.

Table 6. Model fit results.

Fit Index	Structural Equation Model	Confirmatory Factor Analysis
CFI	0.94	0.93
TLI	0.92	0.91
RMSEA	0.05 (90% CI: 0.04–0.06)	0.05 (90% CI: 0.04–0.06)
SRMR	0.04	0.03

presents the results of the structural equation model used to validate the theoretical framework of the three-dimensional mechanism. The model demonstrates a strong fit with the data: the comparative fit index (CFI) is 0.94, exceeding the acceptable threshold of 0.90; the Tucker–Lewis index (TLI) is 0.92, indicating an optimal model fit; the root mean square error of approximation (RMSEA) is 0.05, below the 0.06 threshold, suggesting minimal parameter estimation error; and the standardized root mean square residual (SRMR) is 0.04, well below the 0.08 cutoff, confirming a strong alignment between observed and latent variables. These fit indices collectively support the theoretical model, validating the dynamic causal relationships among tourists, residents, and enterprises.

Path analysis was conducted using AMOS 26.0. In the model, policy support is treated as an exogenous latent variable, while enterprise investment and resident behavior are modeled as endogenous latent variables. The low-carbon effect serves as the final dependent variable. Mediation analysis results are presented in

Table 7. The results of the mediating effect analysis.

Path	Standardized Path Coefficient	95% Confidence Interval	Proportion of Mediating Effect
Policy support → enterprise investment	0.68 ***	[0.52, 0.84]	-
Enterprise investment → resident behavior	0.55 **	[0.31, 0.79]	-
Resident behavior → low-carbon effects	0.72 ***	[0.58, 0.86]	-
Policy support → low-carbon effects (total)	0.42 ***	[0.28, 0.56]	100%
Policy → enterprise → behavior → effects (chain)	0.42 ***	[0.25, 0.61]	81%

Note: *** p < 0.01; ** p < 0.05.

. The path coefficients—policy support → enterprise investment (β = 0.68), enterprise investment → resident behavior (β = 0.55), and resident behavior → low-carbon effect (β = 0.72)—highlight the critical mediating role of enterprises in the transformation process. Although green building subsidies have been effective in raising low-carbon performance, they have also increased operational costs for enterprises. For instance, hotel renovation costs in Zhouzhuang rose by 20%, underscoring the need to balance economic feasibility with environmental benefits.

Table 8. Sensitivity analysis results.

Scenario	Zhouzhuang Composite Score	Tongli Composite Score	Change (%)	Key Impact Indicator
Scenario 1	61.8	57.6	−7.6	Proportion of green buildings
Scenario 2	62.1	54.3	−7.2/5.0	Carbon emission intensity
Scenario 3	61.1	55.8	−8.7/3.1	Proportion of green transportation

reports the results of the sensitivity analysis. Scenario 1 examines variations in policy enforcement intensity. Scenario 2 simulates external shocks, such as energy price fluctuations. Scenario 3 explores changes in tourist behavior and preferences. The findings indicate that the cancellation of Zhouzhuang’s low-carbon building subsidy (a reduction from 50% to 0%) resulted in a 15% decrease in the proportion of green buildings. A 20% increase in natural gas prices led to a regression in the energy structure toward coal usage, causing a 15% rise in carbon emission intensity. Moreover, a decline in tourists’ low-carbon awareness contributed to a 20% drop in the adoption rate of green transportation options.

3.3. Survey Results of Tourists and Residents

Figure 11 displays overall tourist satisfaction levels in the two featured towns. A majority of visitors—71.8%—expressed high satisfaction with their experience in Suzhou’s characteristic towns. By location, 80.4% of tourists in Zhouzhuang reported being satisfied, compared to 69.6% in Tongli. These results suggest that Zhouzhuang provides a more favorable visitor experience across multiple dimensions.

Figure 12 illustrates residents’ awareness of low-carbon consumption. In Suzhou, 77.5% of residents consider low-carbon practices to be highly necessary. This view is shared by 72.4% of residents in Zhouzhuang and 66.3% in Tongli. Figure 13 shows residents’ preferences for low-carbon products, revealing a strong overall willingness to adopt such products across Suzhou’s characteristic towns. Survey results indicate that only 9.8% of residents are reluctant to purchase low-carbon products, reflecting widespread support for sustainable lifestyles. Further analysis reveals that 12.8% of residents in Tongli are unwilling to buy low-carbon products, compared to just 7.2% in Zhouzhuang. These differences may be attributed to varying levels of environmental awareness, economic capacity, and familiarity with low-carbon products.

The data indicate that most residents prefer to purchase low-carbon products, reflecting strong environmental awareness and support for sustainable development. This trend plays a critical role in advancing the low-carbon transformation of characteristic towns. By making eco-friendly purchasing decisions, residents not only reduce their own carbon footprints but also encourage local businesses to invest in the study, development, and supply of low-carbon products, forming a positive feedback loop. Environmental awareness significantly influences residents’ attitudes toward low-carbon consumption. As environmental education and sustainability messaging become more widespread, recognition of low-carbon lifestyles has steadily increased. This heightened awareness motivates individuals to consider environmental impacts in their purchasing behavior. Although Tongli residents exhibit a relatively lower awareness score (66.3%), their reported tourism satisfaction is slightly higher at 80.4%. This may be attributed to a stronger attachment to traditional lifestyles or to resistance toward policy overreach in Zhouzhuang—such as mandatory waste sorting—which may have caused some discontent.

While this study employs a chained mediation model to explore the relationships among policy support, enterprise investment, resident behavior, and low-carbon outcomes, some uncertainty remains regarding causal direction. Reverse causality is possible—improved low-carbon outcomes may prompt governments to intensify policy efforts or encourage businesses to increase investment in green technologies. Additionally, omitted variable bias and multi-collinearity may influence the empirical results. To enhance the reliability of the findings, the study adopted several control strategies, as follows: 1. Control variables: Education and income levels were included in regression models to mitigate omitted variable bias. 2. Robustness checks: Multiple robustness tests were conducted by adjusting model specifications and sample scopes to confirm the stability of the results.

3.4. Policy Suggestion of Low-Carbon Tourism Transformation in Characteristic Towns

Amid growing global concerns over climate change and the pursuit of carbon neutrality, the low-carbon transformation of tourism in characteristic towns has become increasingly urgent. This study proposes a closed-loop system of “policy-driven, community-responsive, and market-innovative” transformation, offering a distinct contrast to Copenhagen’s low-carbon strategy. Whereas Copenhagen emphasizes mandatory regulations and civic engagement—such as the promotion of public transport, cycling, and green buildings—this study, using Zhouzhuang as a case example, highlights the synergy between policy and market mechanisms. Zhouzhuang’s transformation relies on a combined approach of 50% renovation subsidies and a carbon points incentive system for residents, reflecting a collaborative model between government and market actors. While Copenhagen’s success is supported by the high acceptance levels of a wealthy population, China’s characteristic towns must consider economically disadvantaged groups, underscoring the need for localized policy design.

In Zhouzhuang, subsidies for the low-carbon renovation of historic buildings are primarily funded through two channels, with 60% coming from Suzhou’s Low-Carbon Development Fund and 40% coming from a designated portion of the town’s tourism ticket revenue. This funding structure not only ensures relative financial stability for the subsidies but also channels tourism income directly back into the transformation process, creating a virtuous cycle of sustainable development. The subsidy implementation process faced minimal resistance from stakeholders. Although some business owners initially expressed concerns over renovation costs and return on investment timelines, these doubts were alleviated through government-led policy briefings, expert consultations, and successful case demonstrations. From a theoretical perspective, this study advances the institutional analysis of protected areas and tourism destinations proposed by Nyaupane et al. (2022) [47]. Their research suggests that tourism destinations operate within nested institutional arrangements characterized by varying degrees of formality, temporal scales, and spatial jurisdictions. Our findings confirm that low-carbon transformation in characteristic towns is indeed shaped by the interplay between formal institutions (e.g., government regulations and subsidy mechanisms) and informal institutions (e.g., community norms and tourist expectations). The chain mediation model empirically validates the theoretical proposition that institutional alignment across different stakeholder groups significantly enhances the efficiency of sustainability transitions. This extends beyond conventional stakeholder theory by emphasizing not only stakeholder identification and interest management but also the dynamic institutional processes that enable or constrain stakeholder agency in sustainability governance.Over time, business owners have recognized the long-term benefits of low-carbon upgrades, such as reduced energy costs and increased appeal to environmentally conscious tourists, and became active participants in the transformation. When comparing the cost-effectiveness of green building subsidies with public transportation incentives, each approach offers unique advantages and complementary benefits. Green building subsidies have proven effective in driving energy-efficient renovations: for every 1 yuan invested, approximately 3 yuan worth of energy savings and emission reductions are generated. These benefits include decreased energy consumption, lower carbon emissions, and extended building lifespan. As the proportion of green buildings increases, the town’s overall energy structure improves, contributing significantly to long-term carbon reduction goals. Public transport incentives, on the other hand, play a key role in encouraging sustainable travel behavior among residents and tourists. Each newly added electric bus route or upgraded bike lane can reduce local transportation carbon intensity by approximately 15–20%. The resulting benefits include reduced traffic congestion, lower environmental pollution, and enhanced travel experiences. In summary, green building subsidies target supply-side energy efficiency, while public transportation incentives influence demand-side behavioral change. Their combined implementation enables a more efficient and comprehensive approach to low-carbon transformation. Together, they form essential components of the policy framework driving sustainable tourism in China’s characteristic towns.

Bærenholdt et al. (2023) [58] argued that sustainable tourism destinations fundamentally serve as a strategy for tourism development, aiming to distribute tourism activities more evenly across geographic areas to enhance their acceptance among local residents. Urban planning regulations play a key role in aligning city development with environmental protection goals. Saker (2025) [61] emphasized the importance of promoting low-carbon tourism through the use of renewable energy, green mobility, energy-efficient buildings, and improved waste recycling systems. Building on the three-dimensional impact mechanism—centered on tourists, residents, and businesses—and grounded in empirical research in Zhouzhuang, Tongli, and Suzhou, this study puts forward a series of policy recommendations to facilitate the low-carbon transformation of tourism.

Guo and Li (2025) [62] stressed that the effective governance of sustainable tourism required multi-level and cross-sectoral coordination, along with active stakeholder engagement in the decision-making process. In the short term, governments can enhance policy awareness among enterprises by organizing lectures, seminars, and training programs to promote understanding of low-carbon tourism and green management practices. Over the medium-to-long term, governments should formulate a clear strategy for low-carbon tourism transformation in characteristic towns. This strategy should outline specific carbon reduction targets, timelines, and actionable plans. Additionally, a robust legal framework must be established to regulate low-carbon practices, especially in such areas as tourism-related energy consumption and emissions. This legal infrastructure will provide essential support and protection for the implementation of low-carbon initiatives.

Third, social participation is vital to achieving low-carbon tourism transformation in characteristic towns. Raising public awareness is equally important. Educational and promotional campaigns should aim to enhance environmental consciousness, encouraging both residents and tourists to adopt low-carbon practices. Greater community involvement can be fostered by engaging the public in the planning and implementation of low-carbon projects, thereby strengthening their sense of ownership and responsibility. Establishing public oversight mechanisms will also enable residents and tourists to monitor policy implementation and provide constructive feedback. Zhang et al. (2024) [63] noted that volunteer activities—such as promoting clean energy and environmental education—not only raised awareness but also helped mitigate disaster risks and enhance ecological resilience.

The transformation toward low-carbon tourism in characteristic towns demands coordinated efforts across enterprises, government, and society. Enterprises should actively implement low-carbon technologies and adopt green management practices to lead the industry’s transition. Theoretically, this study makes three significant contributions to the sustainable tourism literature. First, it expands stakeholder theory by conceptualizing low-carbon tourism transformation as a dynamic closed-loop system of “policy-driven → community response → market innovation” interactions, moving beyond the static stakeholder identification matrix dominant in previous research. Second, it integrates socio-ecological systems theory with institutional theory through the empirical validation of how governance mechanisms shape sustainability outcomes in characteristic towns. Formal mechanisms, such as regulatory frameworks, operate alongside informal dimensions, including community norms and tourist expectations. The multi-level analysis conducted in this study reveals institutional processes affecting carbon reduction efficiency across different spatial scales. Liburd et al. (2024) [64] emphasize sociocultural value activation as a fundamental component for addressing tourism sustainability challenges in protected heritage contexts. Our findings demonstrate that this activation occurs through policy–enterprise–resident chains influencing carbon outputs. Third, the study advances methodological innovation through the novel application of system dynamics to quantify policy lag effects and stakeholder feedback mechanisms, responding to recent calls by Yang et al. (2023) [60] for more sophisticated analytical approaches to tourism transformation processes.

4. Conclusions

Guided by the goal of carbon neutrality, this study explores pathways for the low-carbon transformation of characteristic towns. It constructs an analytical framework based on the three-dimensional impact mechanism involving tourists, residents, and enterprises, and conducts an empirical investigation using two ancient towns—Zhouzhuang and Tongli in Suzhou, Jiangsu Province—as case studies. As a key component of the tourism industry, the adoption of low-carbon technologies and green management models by enterprises is critical to driving this transformation. A dynamic monitoring indicator system should incorporate key metrics, such as carbon emission intensity, energy consumption, and resident participation. These data provide local governments with real-time feedback, enabling them to accurately assess the progress of low-carbon transformation and make timely adjustments to policy direction and intensity. A policy tracking and evaluation mechanism is also essential for assessing the effectiveness of policy implementation. Its aim is to identify policy strengths and weaknesses, thereby providing a solid foundation for optimization. By regularly reviewing policy execution, governments can gain deep insights into how different policies affect each aspect of the low-carbon transition in characteristic towns, offering precise guidance for future policy iterations. Empirical results show that the average low-carbon score of Suzhou’s characteristic towns is 63.3, exceeding the national average. This score is derived from a comprehensive and rigorous evaluation system that integrates three key dimensions, namely low-carbon performance, economic development, and sustainable tourism, with each scored on a 100-point scale. The findings indicate a moderate level of transformation overall, with notable differences between Tongli and Zhouzhuang. Using system dynamics modeling, this study uncovers dynamic interactions and feedback loops that static analysis methods often overlook. It reveals how policy incentives indirectly influence resident behavior through enterprise investment, offering a dynamic perspective to stakeholder theory. Furthermore, the strong correlation between tourists’ low-carbon awareness and satisfaction (β = 0.73) validates the TPB, particularly the direct impact of “attitude” on behavior, and highlights the moderating role of policy support as an external variable. Based on a comprehensive assessment and analysis, the study draws the following conclusions: 1. The low-carbon transformation of characteristic towns is a vital step toward achieving carbon neutrality and advancing sustainable development. 2. Tourists, residents, and enterprises are all critical actors in this transformation. Tourists’ preferences and low-carbon behaviors have a direct impact on the success of these efforts.

The main innovation of this study lies in the development of the first three-dimensional mechanism model based on multi-agent game theory and system dynamics. This approach breaks away from the traditional one-dimensional analysis often seen in low-carbon tourism research. By quantifying the synergistic effects of policy support, enterprise investment, and resident behavior, the study provides empirical support for socio-ecological systems (SES) theory and stakeholder theory from a dynamic perspective.

Theoretically, this study contributes to the expansion of stakeholder theory by proposing a closed-loop “policy-driven → community response → market innovation” model rooted in multi-agent dynamic interactions. In addition, it introduces a novel application of system dynamics to quantify policy lag effects, offering valuable methodological insights for future studies on sustainable tourism governance. This study primarily examines the current state of low-carbon tourism transformation in selected ancient towns. To comprehensively understand and optimize the transformation process, it is essential to establish dynamic monitoring indicators and implement policy tracking evaluations. Dynamic indicators—such as real-time data on carbon emissions and energy consumption—enable policymakers and enterprises to make timely adjustments, better addressing the challenges encountered during the transition. However, the study has several limitations, as follows: 1. Data collection bias: The study relies heavily on self-reported survey data, which are susceptible to social desirability and recall biases. Future research could enhance data reliability by incorporating sensor-based monitoring and big data analytics. 2. Limited case representation: The study focuses on two ancient towns in Suzhou, which may not fully represent the diverse pathways of low-carbon transformation in other types of characteristic towns, such as those in mountainous, coastal, or industrial heritage regions. Future research should expand the sample to include representative cases from beyond the Yangtze River Delta and employ social network analysis to explore stakeholder interaction patterns across different regions. 3. Exclusion of macroeconomic factors: This study does not account for the dynamic effects of macroeconomic variables, such as GDP fluctuations and energy price changes. Future research should incorporate time series data and apply econometric models to analyze how these factors influence the low-carbon transformation of tourism. The three-dimensional impact mechanism model developed in this study, while providing valuable insights into low-carbon tourism transformation in characteristic towns, faces several important generalizability limitations that must be acknowledged. The model’s empirical validation is primarily based on ancient water towns in the economically developed Yangtze River Delta region, which may limit its applicability to characteristic towns with different geographical, cultural, and economic contexts. As Hu et al. (2024) [65] demonstrated in their visualization study of low-carbon transitions across three distinct tourist attractions in China, the effectiveness of sustainability interventions varies significantly depending on local conditions, tourism infrastructure maturity, and regional economic development levels. The policy mechanisms and stakeholder interactions observed in Zhouzhuang and Tongli may not directly translate to characteristic towns in western or northeastern China, where economic conditions, institutional arrangements, and tourist behavior patterns differ substantially. Furthermore, the model’s focus on heritage-based tourism destinations may not adequately capture the dynamics present in characteristic towns built around modern industries, technological innovation, or agricultural themes, where carbon emission sources and stakeholder priorities may follow entirely different patterns.

The potential for model extensions presents several promising avenues for future research that could address these generalizability constraints while enhancing the analytical framework’s robustness. First, incorporating adaptive governance components would significantly improve the model’s cross-regional applicability, as Farsari (2023) [44] emphasized in her exploration of sustainable tourism governance and complexity research, noting that effective governance systems must be resilient and adaptable to varying local contexts while maintaining core sustainability principles. Second, the current three-dimensional framework could be expanded to include a fourth dimension focusing on technological innovation and digital transformation, recognizing the increasingly critical role of smart tourism technologies in accelerating low-carbon transitions. This extension would be particularly relevant for characteristic towns that leverage digital platforms and IoT systems for visitor management and energy optimization. Third, future research should consider developing a nested modeling approach that captures multi-scale interactions from individual towns to regional tourism clusters, addressing the complex spatial dynamics of carbon flows and policy spillover effects across interconnected destinations. Additionally, as Hu et al. (2024) [65] highlighted in their analysis of regional differences in China’s urbanization modes, the model could benefit from incorporating urban–rural linkage dynamics, particularly for characteristic towns that serve as intermediary spaces between urban centers and rural communities. Such extensions would not only enhance the model’s theoretical sophistication but also provide more nuanced policy guidance for diverse types of characteristic towns across China’s varied geographical and developmental landscapes.