Can the Chinese Cultural Consumption Pilot Policy Facilitate Sustainable Development in the Agritourism Economy?

Abstract

The growing importance of cultural consumption in driving tourism development is reflected in its expanding scale and the simultaneous transformation and upgrading of the cultural industry. This study adopts a multi-period difference-in-differences (DID) model to leverage the quasi-natural experiment created by China’s national cultural consumption pilot policy. Using panel data from 30 provinces spanning the period from 2011 to 2024, we quantitatively assess the policy’s impact on sustainable development within the agritourism economy. Specifically, the study aims to isolate and identify the net effect of the pilot policy on improving the quality and sustainability of agritourism outcomes. Empirical results demonstrate that the implementation of the national cultural consumption pilot policy significantly promotes the development of sustainable agritourism products. Moreover, the policy exerts a notable positive influence on the broader sustainable development of the agritourism economy. These effects are particularly pronounced in the eastern and central regions, while the western region exhibits comparatively weaker impacts. Heterogeneity analysis suggests that the limited effectiveness observed in the western and parts of the central regions may be attributed to constraints such as lower levels of economic development and weaker performance of control variables in promoting sustainability. Overall, this study provides robust empirical evidence supporting the wider implementation and promotion of cultural consumption pilot policies at the national level. The findings offer valuable policy implications for advancing sustainability in the agritourism sector.

1. Introduction

After more than three decades of sustained economic growth at an average annual rate of approximately 10%, China has entered a new phase marked by a transition in both the pace and structure of its economic development. The imperative for sustainable products has become increasingly evident across multiple dimensions of economic and social progress. As a reflection of society’s pursuit of improved living standards, tourism has emerged as a strategically significant sector within the national economy. Widely recognized for its potential to operate within environmentally sustainable frameworks, the tourism industry is often referred to as a “sunrise industry” in the modern service sector. In January 2022, the State Council released the 14th Five-Year Plan for Tourism Development, which emphasizes principles such as “developing tourism through culture” and “highlighting culture through tourism”. This policy framework adopts a comprehensive and systematic approach—strengthening institutional foundations, advancing tourism for public benefit, promoting innovation-led and sustainable development, and prioritizing ecological integrity through scientifically informed resource utilization. Together, these measures provide a strategic roadmap for the sustainable development of China’s tourism industry.

In 2022, the total number of domestic tourists in China reached 2.53 billion, while domestic tourism revenue amounted to CNY 2.0444 trillion—representing year-on-year decreases of 22.1% and 30.0%, respectively. Preliminary estimates indicate that China’s Gross Domestic Product (GDP) grew by 3.0% to reach CNY 121.0207 trillion. The value added in the primary industry increased by 4.1%, the secondary industry by 3.8%, and the tertiary industry by 2.3%. Meanwhile, national income stood at CNY 119.7215 trillion, reflecting a 2.8% increase. Within the service sector, the value added by the accommodation and catering industries was CNY 1.7855 trillion, marking a decline of 2.3%. Additionally, the total passenger transport volume for the year was 5.6 billion trips, indicating a significant year-on-year decrease of 32.7%. The cultural consumption pilot policy drives the agritourism economy through three main pathways: industrial interconnection, in which policies guide the linkage development of intangible cultural heritage workshops and organic farms to create joint ticket products, forming a closed-loop model where cultural experiences boost agricultural product sales; value restructuring, which involves transforming agrarian activities into new consumption targets such as carbon credits (for example, in Zhejiang’s Qingtian model, every CNY 1 of cultural consumption generates CNY 2.3 in agricultural income); and policy empowerment, whereby the Ministry of Culture and Tourism’s “Cultural Industry Empowering Rural Revitalization” initiative, combined with local ecological compensation, has driven the premium rate of carbon-labeled agricultural products to reach 15–30%.

In the realms of cultural tourism, health, and sports, as of the end of 2022, the national cultural and tourism system encompassed a total of 2023 performing arts groups, 3303 public libraries with a circulation of 72,375 million, and 3503 cultural centers. The annual operating income of the national-scale cultural and related industries was CNY 12.1805 trillion, marking a 0.9% increase from the previous year. In 2022, urban residents accounted for 1.93 billion domestic tourists, reflecting a 17.7% decrease, while rural residents accounted for 600 million, indicating a 33.5% decrease. Correspondingly, urban residents’ expenditures amounted to CNY 1.6881 trillion, down by 28.6%, and rural residents’ expenditures were CNY 356.3 billion, experiencing a 35.8% decrease.

One such model is the “Cultural Heritage Tourism Carbon Sink” model, which is driven by intangible cultural heritage, breaking through the limitations of traditional agritourism. This model transforms intangible cultural heritage—such as seasonal farming and local operas—into quantifiable carbon-reduction scenarios for cultural consumption. By designing a “Cultural Heritage Workshop + Organic Farm” combo ticket, visitors can earn farm carbon credits through tie-dyeing experiences, which can be used to offset the carbon emissions from mailing agricultural products. This model directly links cultural consumption behavior with ecological benefits, forming a closed loop of “cultural inheritance–low-carbon consumption–agricultural value enhancement”. By designing cultural elements like intangible heritage skills and traditional cuisine as carbon-reduction carriers, visitors naturally participate in carbon neutrality during their consumption. Agricultural tourism zones can establish dynamic carbon ledgers based on this model, converting cultural consumption data into carbon sink transaction vouchers. This not only enhances the premium value of cultural products but also provides a new solution for achieving carbon neutrality goals in agritourism economy.

In the era of “smart tourism”, revitalization through elements such as red tourism, agritourism economy, intangible cultural heritage, and folk activities inject new vitality into the tourism industry. The sustainable development of the agritourism economy serves as an engine driving the sustainable growth of regional economies and other sectors.

2. Literature Review

Culture is the soul of tourism, and tourism is the carrier of culture. These two entities possess closely intertwined inherent attributes (Alvarez et al. 2010) [1]. Culture and tourism go beyond the visual consumption of refined cultural and artistic products like galleries, theaters, and architecture; they also involve immersing travelers conveniently in the distinctive local atmosphere (Galdini, 2007) [2]. In his discourse, Smith (2009) [3] emphasizes that cultural tourism, as an activity centered around culture, is crucial for driving local economic development and cultural heritage preservation. The research by Hall and Page (1999) [4] affirms the critical role of cultural activities and experiences in the allure of tourist destinations, further confirming the mutually reinforcing relationship between culture and tourism. Therefore, promoting the integrated development of the cultural industry and tourism holds significant importance in fostering sustainable development in the agritourism economy. In April 2021, against the evolving landscape, the Ministry of Culture and Tourism unveiled the “14th Five-Year Plan for Cultural and Tourism Development” to advance the robust growth of the cultural industry and agritourism economy. Recognized as pivotal pillars in fulfilling societal aspirations for an improved quality of life and fostering sustainable development, the cultural industry and tourism assume critical roles. In tandem, cultural consumption is acknowledged as a formidable catalyst propelling the sustainable development of the agritourism economy. A noteworthy milestone in this trajectory was the commencement of the Resident Cultural Consumption Pilot Program by the former Ministry of Culture in June 2015. The release of lists comprising 45 pilot cities in 2016 and 2017 in two batches underscored the nation’s top-level commitment to guiding and prioritizing cultural consumption. Positioned as an integral element in the promotion of sustainable economic development outlined in the 14th Five-Year Plan, the national cultural consumption pilot policy displays innovation by addressing the enhancement of the supply quality of cultural products and services, the cultivation of awareness regarding resident cultural consumption, and the facilitation of sustainable development within the tourism industry.

The sustainable development of the agritourism economy stands as a focal point in economic research, drawing significant attention from academia and industry professionals. In recent years, scholars have delved into profound studies addressing critical issues about agritourism economy development, providing valuable theoretical foundations to enhance our understanding of the tourism market and promote sustainable development. Song and Li (2008) [5] delved into applying modern econometric methods for modeling and forecasting tourism demand. Their analysis juxtaposed these contemporary methods with traditional tourism demand modeling and prediction approaches. By integrating qualitative and quantitative forecasting techniques, incorporating tourism cycles and seasonal analysis, assessment of event impacts, and risk prediction, they markedly improved the accuracy of predictions. This study establishes a robust methodological underpinning for subsequent in-depth investigations into the ramifications of cultural consumption pilot policies on the agritourism economy. In the context of sustainable development, diverse scholars have put forth various metrics and evaluation methodologies. Tahiri and Kovai (2012) [6] crafted a comprehensive evaluation indicator system for assessing the quality of tourism industry development from the perspectives of tourists, tourism enterprises, and regional impacts. Fatma et al. (2016) [7] systematically formulated a scientifically grounded evaluation indicator system for assessing the quality of tourism industry development. This comprehensive system encompasses critical dimensions, including the quality of the tourism development environment, the quality of tourist travel experiences, the development quality of tourism enterprises, the development quality of the overall tourism industry, and the development quality of tourism destinations. The song further introduced a methodology and approach for constructing the Tourism Industry Development Quality Index in a parallel effort. Njoroge and J.M (2017) [8] employed survey questionnaires to establish an evaluation system, adopting the perspective of ecological civilization. Utilizing regional tourism in Inner Mongolia as a case study, Yang conducted a meticulous and systematic assessment, providing valuable empirical evidence for his ecological civilization-based evaluation framework. Latkova and Vogt (2016) [9], following a comprehensive review and evaluation of both domestic and international indicators for assessing ecological civilization construction, proposed an optimized approach to enhance the evaluation indicator system for ecological civilization construction in China. The authors outlined eight principles for selecting indicators and, utilizing currently available ecological civilization-related data, conducted preliminary attempts using the principal component analysis method to formulate the framework of the indicator system. Zhou (2024) [10] developed a tourism quality evaluation indicator system by adopting an ecological civilization perspective. This system, structured around four dimensions—tourism environmental quality, tourism resource quality, tourism service quality, and tourism attraction capacity—encompassed 26 indicators covering aspects such as pollution status, ecological background, scenic resources, cultural resources, accommodation conditions, service capacity, service level, tourism flow, and tourism revenue. Cassia (2020) [11], through the integration of panel data spanning from 2002 to 2017, employed a comprehensive analytical approach involving three-stage DEA models, super-efficiency SBM models, and Tobit regression methods to conduct an in-depth analysis of efficiency changes in the tourism industry across 31 provinces. Peng and Cheng (2022) [12] applied the entropy method to gauge sustainable economic development from diverse perspectives. The assessed indicators included economic efficiency, structural benefits, development potential, and the extent of resource utilization. Li and Hu (2021) [13] advocate for a multidimensional approach to evaluating sustainable economic development. They assert that economic development attains high quality when driven primarily by innovation, inherent coordination, widespread adoption of green practices, embracing openness as an essential path, and prioritizing sharing as the fundamental objective. The assessment and measurement of sustainable development in China’s agritourism economy have been persistent concerns for scholars. Drawing parallels with the evaluation and measurement of sustainable development, domestic scholars have predominantly concentrated on assessing and measuring sustainable development in the agritourism economy, focusing on efficiency and regional coordination of tourism economic development across various regions of China.

The “Measuring Sustainable Development of China’s agritourism economy” paper proposes an assessment framework comprising five dimensions—innovation, coordination, green practices, openness, and sharing—to gauge the extent of sustainable development in China’s agritourism economy. The authors employ methodologies such as constructing a variation index model to validate substantial regional disparities and sustainable development in China’s agritourism economy. In a study by Knezic et al. (2022) [14], the scholars employed three-stage DEA and super-efficiency SBM models. Variables such as the number of travel agencies and the fixed asset value of the tourism industry were chosen to assess the overall economic efficiency of the tourism industry. The findings indicate a consistent enhancement in management and production technologies within the agritourism industry. However, significant regional disparities in tourism economic efficiency are evident, reflecting uneven regional coordination in the sustainable development process in China’s agritourism economy. Scholars Chassang et al. (2024) [15] categorized indicators for evaluating the sustainable development of the agritourism economy into three domains: agritourism product quality, tourism service quality, and facility quality. Utilizing a blend of the entropy method and expert surveys with assigned weights, a scoring system was established to appraise the development of different indicators in Suzhou. This approach identifies strengths and weaknesses across various indicators, offering insights into the influence of the constructed indicator system on the sustainable development of agritourism economy. This methodology presents a novel perspective for scrutinizing the overall economic aspects of China’s tourism industry. Bacter (2025) [16], in the paper titled “Cultural Industry Agglomeration Empowering sustainable Development of the agritourism economy”, establishes a comprehensive framework for evaluating the sustainable development of the agritourism economy. The framework incorporates primary indicators such as coordinated economic and green environmental development. Secondary indicators include metrics such as the number of practitioners in the tourism industry, the contribution of the tourism industry to GDP, and urbanization rates. The author employs the entropy weight method to assess the level of sustainable development in the agritourism economy. Empirical analysis uncovers a significant regional disparity in the level of sustainable development in the agritourism economy, with sustainable development closely correlated with local economic and green development in each region.

The existing body of research has primarily examined the factors influencing the sustainable development of the agritourism economy from two perspectives: internal governance within regions and external factors. Within internal governance studies, Hongchang Zhang (2019) [17] observed that optimizing the structure of tourism supply, enhancing tourism consumption concepts, and improving technological governance capabilities are instrumental in promoting sustainable development in the tourism industry. Xinyue Wang et al. (2020) [18] highlighted tourism core elements, transportation, economic development, and transformation as pivotal factors influencing the sustainable development of the tourism industry. Welford et al. (1999) [19] noted that implementing supply chain management and destination management for local tourism industries can effectively enhance the provision of tourism services, thereby fostering the efficient development of the agritourism economy. Yu Fawen et al. (2020) [20] proposed six key aspects to facilitate sustainable development in the agritourism economy: comprehensive and scientific rural development planning, sustainability of resources, industrial integration, talent team construction, tourism products and services, and safeguard measures. Castanho et al. (2023) [21], in their study analyzing pilot projects implemented in the Azores, found that the governance strategies of regional authorities and the entrepreneurial spirit of local small and medium-sized enterprises play crucial roles in tourism development. They suggest the need to strengthen sustainable development initiatives while emphasizing diverse designs for tourism products, thereby stimulating improvements in the local agritourism economy. Santos et al. (2022) [22] found, through their study of the archipelago, that implementing creative tourism as a strategic approach is more conducive to developing the local tourism industry after the outbreak of COVID-19. It is also more effective in stimulating sustainable growth in the agritourism economy. Lu et al. (2021) [23] similarly propose that creative tourism is a new type of tourism, and critical determinants for developing creative tourism include local heritage, service quality, and participatory experiences. De and Jelinčić (2016) [24] introduce a novel concept, “participatory experience tourism”, to better explain positive forms of visitor engagement arising from contemporary societal creativity and social transformations, enhancing understanding of critical elements in the experiential value chain. Connell (2013) [25], by outlining the development of medical tourism, suggests that many medical tourism activities are short-distance and dispersed, constituting a growing part of the globalized medical industry. Mikulić et al. (2017) [26], focusing on camping tourism, study the driving factors influencing camping tourism, emphasizing decisive camping site attributes for site selection and experience to assist managers in planning marketing activities tailored to this specific tourism submarket. Hadad (2019) [27], through research, asserts that the tourism industry is a crucial economic component of sustainable development and examines how the agritourism economy can be developed within the context of a sustainable bioeconomy.

In studies examining external factors, Dwyer and Kim (2003) [28] employed a competitive model to scrutinize the competitiveness of destinations and their influencing factors from the perspective of sustainable tourism. They compared the development status of tourism economies under different regional conditions, emphasizing the crucial role of sustainable development in the agritourism economy. When formulating and implementing national cultural consumption pilot policies, attention should be given to protecting cultural heritage and promoting sustainable development in society and the environment. This approach can increase the number of tourists, boost regional tourism income, and promote socio-economic prosperity. Ma Hongmei and Hao Meizhu (2020) [29], using the difference-in-differences method, found that the opening of high-speed rail significantly promotes tourism development and sustainable economic development along the rail lines, especially playing a significant driving role in the development of the tourism industry in underdeveloped areas. Zhang and Gu (2022) [30] used two indicators, industrial structure rationalization and industrial structure maturity, to measure industrial structure upgrading. They treated the construction of cultural consumption pilot cities as an exogenous impact in a quasi-natural experiment. Employing a difference-in-differences model, they investigated the net effects of industrial structure upgrading. The study revealed that constructing cultural consumption pilot cities significantly promotes the maturation and rationalization of urban industrial structures. This is achieved through policy effects that enhance resident consumption preferences on the demand side and upgrade consumption-supporting facilities on the supply side. Consequently, this policy fosters synergies between supply and demand and promotes sustainable upgrading of the local tourism industry through coordinated supply and demand and industrial linkage effects. Budiarti and Listyanti (2015) [31] found that the cultural consumption pilot policy can promote regional tourism economic growth. Huang Yingzuo and Wang Shan (2022) [32] discovered that the “dual-carbon” development strategy could facilitate the low-carbon development of tourism products, propelling sustainable development in the agritourism economy. Shi Bo and Ren Baoping (2021) [33], along with Feng Feng (2022) [34], empirically found that large-scale sports events, exemplified by the National Games and the Olympic Games, can promote sustainable development in the agritourism economy by reshaping city images and facilitating structural upgrades and green development.

In the sustainable development of agricultural tourism, Manioudis and Meramveliotak’s (2022) research of the classical political economy provides an analytical backbone for certain elements, including the important role of history, the necessity of an interdisciplinary approach, and the analytical priority of social classes that could be critical in enriching sustainable development studies [35].

Tourism eco-efficiency (TEE) is a pivotal metric for assessing tourism’s sustainability and the balance between human activities and the environment, significantly influencing regional economic growth (Guo, 2024) [36]. Watene and Yap (2015) think the possible contribution of indigenous people to sustainable development impacts human development and they consider how the perspectives of Maori and indigenous people change our understanding and approach to sustainable development [37]. Sarkar et al. (2022) [38] found that attitudes towards sustainable agriculture, perceived behavioral control and perceived self-identity have a gradual and fundamental impact on adoption behavior, and affect farmers’ intention to adopt specific production mechanisms, and are conducive to the development of agritourism economy.

Building upon these findings, this study utilizes panel data from 30 provinces in China from 2011 to 2024. Employing a multi-period difference-in-differences approach, the research quantitatively evaluates the impact of the national cultural consumption pilot policy on the sustainable development of the agritourism economy.

3. Research Design

3.1. Model Specification

The national cultural consumption pilot policy was phased in in 2016 and 2017. It presented a conducive quasi-natural experiment for probing its influence on the agritourism economy’s sustainable development by applying a difference-in-differences model. A multi-period difference-in-differences methodology facilitates the concurrent management of regional disparities between pilot and non-pilot provinces and temporal variations pre- and post-policy implementation. This approach enhances the precision in isolating the net effects of the national cultural consumption pilot policy on the sustainable development of the agritourism economy. The double-difference model is formulated as follows:

$${TOUR}_{it}={\alpha }_0{+\alpha }_1{PPCC}_{it}+{\alpha }_2{Controls}_{it}+{\lambda }_i+{\phi }_t+{\epsilon }_{it}$$

(1)

Among this model, ${TOUR}_{it}$ represents the level of sustainable development in the agritourism economy for region i in year t. ${PPCC}_{it}$ is the virtual variable for the national cultural consumption pilot policy, which is an interaction term between the experimental group virtual variable and the time virtual variable for the policy. If a city in region i becomes a part of the national cultural consumption pilot policy in year t, then the value is 1 for that year and subsequent years. Otherwise, it is 0. α1 measures the impact of the national cultural consumption pilot policy on the sustainable development of the agritourism economy. ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ represents the control variables. λi and φt represent individual fixed effects and time fixed effects, respectively. εit is the random disturbance term.

3.2. Description of the Explained Variable

The dependent variable in this study is the level of sustainable development in the agritourism economy (${TOUR}_{it}$). It is calculated using the entropy method based on the indicator system, as shown in

Table 1. Indicator system and weighting for the sustainable development level of the agritourism economy.

Primary Indicators	Secondary Indicators	Indicator Attributes	Weight
Green Environmental Development	Green Coverage Area	+	0.0177
	Chemical Oxygen Demand (COD) in Wastewater	−	0.0097
	Sulfur Dioxide Emissions	−	0.0075
	Waste Treatment Rate	+	0.0500
Tourism Industry Development	Number of Travel Agencies	+	0.0356
	Number of Tourist Hotels	+	0.0302
	Number of Class A or Above Tourist Attractions	+	0.0355
	Domestic and Foreign Tourism Revenue	+	0.0758
	Ratio of Domestic and Foreign Tourism Revenue/GDP	+	0.0772
Level of Openness to the Outside World	Number of Inbound Tourists	+	0.1109
	Foreign Exchange Earnings from Tourism	+	0.0977
Transportation and Transit Conditions	Employment in the Air Transport Industry	+	0.0945
	Road Passenger Turnover	+	0.0561
	Railway Passenger Turnover	+	0.0395
Development of Innovation Environment	Research and Development Expenditure Intensity	+	0.0453
	Number of Patent Applications Filed	+	0.1044
	Number of Patents Granted	+	0.1122

.

3.3. Description of the Core Explanatory Variable

The central explanatory variable in this paper is the virtual variable of the national cultural consumption pilot policy (${PPCC}_{it}$). It is the product of the experimental group virtual variable and the national cultural consumption pilot policy time virtual variable. If a city in region i becomes a national cultural consumption pilot city in year t, the value is 1 for that year and subsequent years; otherwise, it is 0.

3.4. Description of the Control Variable

The paper selects the following variables as control variables: ① regional development level, including unemployment rate and urbanization rate; ② infrastructure level, including urban transportation level, represented by the number of public transport vehicles per ten thousand people; ③ human resources level, including the proportion of higher education, represented by the proportion of those with education above college level; ④ market openness level, including the degree of openness to the outside world and marketization level, represented by the proportion of foreign investment to GDP and the Fan Gang Marketization Index; and ⑤ innovation and transformation level, including green innovation level and manufacturing upgrading, represented by the logarithm of the number of green invention patents and the proportion of high-tech industry output value to industrial output value. The explanation of control variables is shown in

Table 2. Control variable explanations.

Variable Dimensions	Variable Names	Variable Descriptions	Unit	Data Source
Regional development level	Unemployment rate		%	China Statistical Yearbook
	Urbanization rate		%
Infrastructure level	Urban transportation level	Number of public transport vehicles per ten thousand people	vehicles
Human resources level	Proportion of higher education	Proportion of population with tertiary education	%
Market openness level	Degree of openness to foreign investment	Ratio of foreign investment to GDP	%
	Level of marketization	Fan Gang’s Marketization Index	—	Wind Database
Innovation transformation level	Green innovation level	Logarithmic value of green invention patents	—	China National Research Data Service (CNRDS)
	Manufacturing industry upgrade	Proportion of output from high-tech industries to total industrial output	%	China High-Tech Industry Statistical Yearbook

, where the unemployment rate is considered as a negative indicator.

3.5. Data Sources

Due to severe data deficiencies in Tibet, this study employs panel data from 2011 to 2024 for 30 provinces in China for empirical analysis. Within this sample, 26 provinces were included in the national cultural consumption pilot policy between 2016 and 2017. The list of provinces included in the national cultural consumption pilot policy is obtained from the official website of the Ministry of Culture and Tourism of China. The data on the level of sustainable development in tourism are calculated based on the indicator system. In contrast, the data for control variables are obtained from the China Statistical Yearbook, Wind database, China National Research Data Service (CNRDS), and the China High-Tech Industry Statistical Yearbook. Descriptive statistics for the main variables are presented in

Table 3. Descriptive statistics of main variables.

Variable Name	Variable	Obs	Min	Max	Mean	Sd.
Sustainable development of agritourism economy	$\mathrm{T}\mathrm{O}\mathrm{U}\mathrm{R}$	300	0.0273	0.6985	0.1405	0.1010
National cultural consumption pilot policy	$\mathrm{P}\mathrm{P}\mathrm{C}\mathrm{C}$	300	0.0000	1.0000	0.4267	0.4954
Unemployment rate	$\mathrm{U}\mathrm{E}\mathrm{M}$	300	1.2000	4.6000	3.2593	0.6401
Urbanization rate	$\mathrm{U}\mathrm{R}\mathrm{B}$	300	35.0300	89.6000	59.0064	12.2183
Urban transportation level	$\mathrm{V}\mathrm{E}\mathrm{H}$	300	7.0500	26.5500	12.7734	2.9816
Proportion of higher education	$\mathrm{H}\mathrm{E}\mathrm{D}$	300	8.0000	62.2000	19.3903	9.9519
Degree of openness to foreign investment	$\mathrm{O}\mathrm{P}\mathrm{E}$	300	0.0011	0.2767	0.0470	0.0521
Level of marketization	$\mathrm{M}\mathrm{A}\mathrm{R}\mathrm{K}$	300	2.3300	12.0000	6.8281	2.0285
Green innovation level	$\mathrm{G}\mathrm{T}\mathrm{I}$	300	0.0807	2.7759	1.1493	0.5938
Upgrading of manufacturing industry	$\mathrm{M}\mathrm{A}\mathrm{U}$	300	−0.0001	0.1316	0.0285	0.0212

.

4. Empirical Analysis

4.1. Baseline Regression

The empirical test was conducted based on Model (1) using a multi-period difference-in-differences method, and the regression results are presented in

Table 4. The impact of the national cultural consumption pilot policy on the sustainable development of the agritourism economy.

Explanatory Variable	Level of Sustainable Development in Agritourism Economy $\left(\mathbf{T}\mathbf{O}\mathbf{U}\mathbf{R}\right)$
	Column (1)	Column (2)
$PPCC$	0.00408 * (1.93)	0.00732 *** (2.81)
$UEM$		−0.00410 ** (−2.06)
$URB$		0.00006 (0.08)
$VEH$		0.00045 (0.94)
$HED$		−0.00121 *** (−2.87)
$OPE$		−0.38398 ** (−2.56)
$MARK$		0.01527 *** (13.10)
$GTI$		0.00674 (0.84)
$MAU$		0.48042 *** (4.41)
$\_cons$	0.22458 *** (37.96)	0.17197 *** (2.87)
Individual	Control	Control
Year	Control	Control
Obs	300	300
Adjusted R-squared	0.9574	0.9647

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. As shown in Table 4, Column (1), without including control variables and with fixed individual and time effects, the coefficient of the virtual variable for the national cultural consumption pilot policy is significantly positive. In Column (2) of Table 4, with the addition of control variables and fixed individual and time effects, the coefficient of the virtual variable for the national cultural consumption pilot policy remains significantly positive. This indicates that implementing the national cultural consumption pilot policy significantly promotes the sustainable development of the agritourism economy.

Considering the control variables, improvements in regional development, market openness, and innovation transformation significantly facilitate the sustainable development of the agritourism economy. The impact of infrastructure-level improvement on the sustainable development of the agritourism economy is not significant. An increase in human resources harms the sustainable development of the agritourism economy, possibly due to the limited sample size and the relatively few selected control variables related to the human resources level.

4.2. Heterogeneity Analysis

Based on Model (1), a heterogeneity analysis of the impact of the national cultural consumption pilot policy on the sustainable development of the agritourism economy in the eastern, central, and western regions was conducted. The regression results are presented in

Table 5. The impact of the national cultural consumption pilot policy on sustainable development of the agritourism economy: heterogeneity analysis.

Explanatory Variable	Level of Sustainable Development in Agritourism Economy (TOUR)
	Eastern Region	Central Region	Western Region
$\mathrm{P}\mathrm{P}\mathrm{C}\mathrm{C}$	0.02583 ** (2.04)	0.01686 *** (3.09)	−0.00642 ** (−2.17)
$\mathrm{U}\mathrm{E}\mathrm{M}$	0.00087 (0.11)	0.00120 (0.71)	−0.00104 (−0.29)
$\mathrm{U}\mathrm{R}\mathrm{B}$	0.00890 *** (6.29)	0.00178 (0.49)	0.00101 (0.85)
$\mathrm{V}\mathrm{E}\mathrm{H}$	0.00096 (0.45)	−0.00034 (−0.30)	−0.00104 (−0.81)
$\mathrm{H}\mathrm{E}\mathrm{D}$	0.00096 (1.40)	−0.00025 (−0.20)	−0.00199 *** (−3.20)
$\mathrm{O}\mathrm{P}\mathrm{E}$	−1.34805 *** (−6.34)	−0.18166 (−0.27)	0.02472 (0.41)
$\mathrm{M}\mathrm{A}\mathrm{R}\mathrm{K}$	0.02899 *** (9.99)	0.00515 (0.58)	0.00059 (0.25)
$\mathrm{G}\mathrm{T}\mathrm{I}$	0.02653 (1.15)	0.00628 (0.35)	−0.00525 (−1.09)
$\mathrm{M}\mathrm{A}\mathrm{U}$	0.70722 *** (5.53)	0.36601 (1.29)	0.05830 (0.45)
$\_\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}$	−0.66572 *** (−6.34)	0.04255 (0.15)	0.12783 * (1.92)
Individual	Control	Control	Control
Year	Control	Control	Control
Obs	130	60	110
Adjusted R2	0.9698	0.9716	0.9217

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. Table 5 shows that after controlling for fixed individual and time effects, the coefficients of the national cultural consumption pilot policy dummy variables for the eastern and central regions are significantly positive. In contrast, the coefficient for the western region is significantly harmful. This indicates that the implementation of the national cultural consumption pilot policy significantly promotes the sustainable development of the agritourism economy in the eastern and central regions but has a significant inhibitory effect on the sustainable development of the agritourism economy in the western region. This may be because many cities in the western region did not become national cultural consumption pilot cities. Consequently, the impact of policy implementation on the sustainable development of the agritourism economy in the western region is limited. Additionally, due to constraints related to economic development and infrastructure levels in the western region, the national cultural consumption pilot policy did not exhibit a significant positive effect.

Looking at the control variables, the development level, market openness level, and innovation transformation level significantly promote the sustainable development of the agritourism economy in the eastern region. Control variables do not significantly impact the sustainable development of the agritourism economy in the central region. Human resource level has a significantly inhibiting effect on the sustainable development of the agritourism economy in the western region. This may be because the years after the implementation of the national cultural consumption pilot policy are relatively short, and control variables have not produced a significant promoting effect on the sustainable development of the agritourism economy in the central and western regions, possibly due to factors such as economic development levels.

Pilot policies for cultural consumption have not adequately considered the economic foundation and resource endowments of western regions, lacking detailed measures tailored to local conditions. The western region relies on government subsidies and traditional loans, but these funds arrive slowly and in insufficient amounts. Moreover, a higher proportion of the population has low to middle incomes, with their marginal propensity to consume significantly influenced by income fluctuations. Post-pandemic preventive savings have increased, further squeezing spending on cultural and tourism consumption. Therefore, pilot policies for cultural consumption have had adverse or restrictive effects on agricultural tourism in the western region.

5. Robust Test

5.1. Parallel Trend Test

The prerequisite for estimating the effectiveness of the policy using the difference-in-differences method is that the experimental group and the control group have the same growth trend before being impacted by the policy. Therefore, it is necessary to conduct a parallel trend test on the dependent variable. This study set a virtual variable for each year before implementing the national cultural consumption pilot policy and multiplied it with the experimental group’s virtual variable. The resulting virtual variable was then regressed against the sustainable development of the agritourism economy, and the results are shown in Figure 1. As Figure 1 indicates, before implementing the national cultural consumption pilot policy, the coefficients of the virtual variable multiplied by the year and the experimental group were primarily negative and insignificant. Hence, it can be inferred that the model largely satisfies the parallel trend assumption.

5.2. Placebo Test

The empirical analysis presented in the previous sections demonstrates that the implementation of the national cultural consumption pilot policy significantly promotes the sustainable development of the agritourism economy. However, these findings may be influenced by potential random factors or unobserved heterogeneity. To address this concern and validate the robustness of the results, a placebo test is conducted.

Using the official list of pilot regions, we randomly assign the treatment status 500 times to generate pseudo-treatment groups. For each random assignment, a baseline regression is performed. This procedure yields a kernel density distribution of the estimated coefficients for the core explanatory variable across the 500 placebo samples, as illustrated in Figure 2. The distribution is then compared to the actual estimated coefficient of the national cultural consumption pilot policy, as reported in

Table 6. The impact of national cultural consumption pilot policy on the sustainable development of the agritourism economy.

Explanatory Variable	Agritourism Economic Sustainable Development Level $\left(\mathit{T}\mathit{O}\mathit{U}\mathit{R}\right)$
	Results Without Changing the Policy Shock Time	Results with Changing the Policy Shock Time
	Column (1)	Column (2)
$\mathrm{P}\mathrm{P}\mathrm{C}\mathrm{C}$	0.00732 *** (2.81)	0.01800 (1.58)
$\mathrm{U}\mathrm{E}\mathrm{M}$	−0.00410 ** (−2.06)	−0.03200 *** (−5.14)
$\mathrm{U}\mathrm{R}\mathrm{B}$	0.00006 (0.08)	0.00100 * (1.73)
$\mathrm{V}\mathrm{E}\mathrm{H}$	0.00045 (0.94)	0.00200 (1.43)
$\mathrm{H}\mathrm{E}\mathrm{D}$	−0.00121 *** (−2.87)	−0.00200 ** (−2.24)
$\mathrm{O}\mathrm{P}\mathrm{E}$	−0.38398 ** (−2.56)	0.55300 *** (4.30)
$\mathrm{M}\mathrm{A}\mathrm{R}\mathrm{K}$	0.01527 *** (13.10)	0.02300 *** (11.15)
$\mathrm{G}\mathrm{T}\mathrm{I}$	0.00674 (0.84)	0.08900 *** (8.66)
$\mathrm{M}\mathrm{A}\mathrm{U}$	0.48042 *** (4.41)	0.61400 ** (2.44)
Individual	Control	Control
Year	Control	Control
Obs	300	300
Adjusted R-squared	0.9647	0.7400

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

.

As shown in Figure 2, there is a clear and statistically significant deviation between the distribution of the placebo coefficients and the actual estimated coefficient. This suggests that the observed policy effect is unlikely to be driven by unobserved provincial characteristics or random chance. The placebo test thus reinforces the conclusion that the national cultural consumption pilot policy has a significant and robust effect in promoting the sustainable development of the agritourism economy.

5.3. Counterfactual Test

Although the empirical analysis conducted earlier in this study has demonstrated that the implementation of the national cultural consumption pilot policy significantly promotes the sustainable development of the agritourism economy, it is also plausible that these results could be attributed to other policies or influencing factors.

To further examine the robustness of the empirical results, this study employs a counterfactual test method to investigate whether the core explanatory variable remains significant when the national cultural consumption pilot policy is not implemented. If it remains significant, it suggests the presence of other unobserved factors promoting the sustainable development of the agritourism economy. If it is not significant, it demonstrates that implementing the national cultural consumption pilot policy significantly and robustly promotes sustainable development in the agritourism economy. The study advances the implementation year of the national cultural consumption pilot policy by one year and re-conducts the regression. The results are presented in Table 6, where Column (1) represents the empirical analysis results without changing the policy shock time, and Column (2) represents the results with the altered policy shock time. As shown in Column (2) of Table 6, changing the policy shock time results in a non-significant positive coefficient for the National Cultural Consumption Policy virtual variable. This indicates that the model complies with the counterfactual assumption, and the implementation of the national cultural consumption pilot policy significantly and robustly promotes the sustainable development of the agritourism economy.

6. Conclusions and Recommendations

Drawing on a comprehensive panel dataset covering 30 provinces in China from 2011 to 2024, this study employs a multi-period difference-in-differences (DID) approach to examine the impact of the national cultural consumption pilot policy on the sustainable development of the agritourism economy. The analysis yields several key findings:

The implementation of the national cultural consumption pilot policy serves as a powerful catalyst for promoting sustainable development in the agritourism sector. While the policy has produced significant positive effects in the eastern and central regions, its impact remains relatively limited in the western region.

Based on these compelling empirical results, the study offers the following policy recommendations:

First, continue the steadfast implementation of the national cultural consumption pilot policy. In addition to supporting experimentation in designated pilot cities, it is crucial to leverage their policy advantages to accelerate sustainable growth in domestic tourism. Furthermore, efforts should be made to expand both the conceptual scope and practical application of cultural consumption pilot programs.

Second, adopt regionally differentiated strategies tailored to the specific socio-economic contexts of each region. In particular, while extending policy support to the western region, it is essential to strengthen its economic foundations and infrastructure to enable effective policy uptake. At the same time, the economic vitality and innovation capacities of the eastern region should be strategically channeled to support the development of the central and western regions, thereby fostering balanced and coordinated regional development.

Third, seize the transformative opportunities of the “smart tourism” era and the integration of digital technologies with the tourism sector. Accelerate the construction of sustainable tourism infrastructure, promote the development of smart cities and scenic areas, enhance service capabilities within the industry, and leverage digitalization and intelligent technologies to stimulate cultural consumption. These initiatives are expected to become key drivers of sustainable development in the agritourism economy.

Finally, agritourism in western China needs to achieve the transformation from resource dependence to innovation drive through systematic measures such as targeted policy support, special subsidies for land use optimization, infrastructure upgrading and digital transportation, product innovation and cultural and technological integration, diversified financing, PPP cooperation, and local training and community sharing for talent cultivation.