Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China
Abstract
:1. Introduction
2. Literature Review
2.1. The Relationship between ATI and AGTFP
2.2. The Mediation Effect of Agricultural Industrial Structure Adjustment Index
2.3. The Threshold Effect of Human Capital Level
3. Methodology
3.1. Measurement of AGTFP and ATI
- (1)
- Determine the ratio of the th indicator for the th province using the formula , where represents the total number of provinces.
- (2)
- Calculate the information entropy of the th indicator: .
- (3)
- Compute the degree of divergence of the th indicator: .
- (4)
- Obtain the entropy weight of the th indicator using the formula , where is the total number of indicators and .
3.2. Empirical Models
3.2.1. Baseline Model
3.2.2. Mediating Effect Model
3.2.3. Threshold Effect Model
3.3. Variable Selection
4. Empirical Results
4.1. Descriptive Statistics
- (1)
- AGTFP has a mean value of 1.3741, showing a positive trend in agricultural productivity. The maximum value of 3.1306 suggests that some regions have achieved substantial improvements in green agricultural productivity, while the minimum value of 0.6494 points to areas with significant room for improvement.
- (2)
- The ATI levels show significant differences. Some regions achieved a higher degree of integration (maximum 0.4601), and others are still in the early stages (minimum 0.0665). The differences in ATI levels provides an excellent opportunity to examine its impact on AGTFP.
- (3)
- The mean value of the AISAI (0.4776) indicates that approximately 50% of the agricultural output is derived from non-traditional agricultural activities, indicating a significant level of diversification within the sector. The range of the AISAI values (0.0940 to 0.6489) implies that different regions are in various stages of agricultural restructuring, which may play a crucial role in the ATI-AGTFP relationship.
- (4)
- The control variables display some remarkable characteristics. The relatively low mean value of 0.0213 for HCL suggests that further investment in higher education is necessary to support agricultural development. The TD in the agriculture sector shows a very low level of reliance on international trade, with a mean value of 0.0257. However, there is substantial variation observed across different regions. These factors may influence the ATI-AGTFP relationship.
4.2. Baseline Model Regression Results
4.3. Mediating Effect Analysis
4.4. Threshold Effect Analysis
5. Discussion
- (1)
- The AGTFP values (0.6494–3.1306) show that there is a regional difference in AGTFP. Regions with higher AGTFP values may benefit from advanced agricultural technologies, efficient resource management, and policy support. This is consistent with the findings of Chen et al. [52], which point out technological advancements as the main driver of increased agricultural productivity. These advancements may involve the adoption of precision farming techniques, sustainable water management systems, and environmentally friendly pest control methods to increase yields while minimizing environmental impacts. In contrast, areas with lower AGTFP values might face problems that include limited access to modern agricultural technologies, inefficient resource use, or environmental degradation. To address these issues, policymakers should focus on technology transfer programs, sustainable resource management training, and targeted environmental policies, as suggested by Adetutu and Ajayi [41].
- (2)
- The different levels of ATI provide a valuable opportunity to examine the impact of ATI on AGTFP [44]. Regions with high levels of ATI have likely succeeded in leveraging their natural and cultural resources to create attractive agritourism products, which has led to increased agricultural productivity through diversification and value addition. This finding aligns with Barbieri and Mshenga’s [18] research, which identified a positive relationship between agritourism and sustainable agricultural practices. To promote ATI in less developed regions, policymakers should prioritize infrastructure development, marketing support, and capacity building initiatives that enable farmers to effectively engage in tourism activities.
- (3)
- The AISAI values ranged between 0.0940 and 0.6489, with an average value of 0.4776. It indicates a change in the industry’s structure towards non-traditional agricultural operations. According to Flanigan et al. [19], agritourism has the potential to facilitate the reorganization of the agricultural industry, and this study confirms their viewpoint. Firstly, a higher AISAI value indicates a more diversified agricultural sector, which leads to an increased ability to withstand economic shocks and changes in the market [53]. This diversification allows farmers to spread risk across multiple income streams, which may result in more stable and sustainable agricultural productivity growth. Secondly, regions with higher AISAI values are likely to have developed complementary skills and infrastructure that can support both agricultural and tourism activities. The collaboration between different elements can lead to more efficient resource allocation and improved overall productivity, as proposed by Tew and Barbieri [5]. Thirdly, a diversified agricultural structure may attract a wider range of tourists by providing a multitude of different experiences and products, thereby increasing the possibilities for tourism integration and its associated benefits [54]. Lastly, the shift towards non-traditional agricultural activities often includes higher value-added products and services, which can contribute to increased agricultural productivity and profitability when integrated with tourism [11].
6. Conclusions and Policy Recommendation
6.1. Conclusions
6.2. Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
ID | Province | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Beijing | 0.2333 | 0.2367 | 0.2611 | 0.2739 | 0.2977 | 0.3179 | 0.3209 | 0.3870 | 0.4138 | 0.4014 | 0.3914 |
2 | Tianjin | 0.1446 | 0.1513 | 0.1644 | 0.1692 | 0.1871 | 0.2050 | 0.1949 | 0.2974 | 0.3200 | 0.3191 | 0.3466 |
3 | Hebei | 0.2545 | 0.2581 | 0.2629 | 0.2698 | 0.2900 | 0.3012 | 0.3086 | 0.3348 | 0.3540 | 0.3433 | 0.3542 |
4 | Shanxi | 0.1586 | 0.1685 | 0.1850 | 0.1961 | 0.2033 | 0.2137 | 0.2219 | 0.2772 | 0.3134 | 0.2973 | 0.2830 |
5 | Inner Mongolia | 0.1752 | 0.1852 | 0.1988 | 0.2045 | 0.2133 | 0.2238 | 0.2321 | 0.2599 | 0.2728 | 0.2807 | 0.2850 |
6 | Liaoning | 0.2544 | 0.2624 | 0.2810 | 0.2901 | 0.2955 | 0.3050 | 0.3067 | 0.3406 | 0.3538 | 0.3469 | 0.3482 |
7 | Jilin | 0.1847 | 0.1911 | 0.2174 | 0.2242 | 0.2424 | 0.2465 | 0.2570 | 0.2812 | 0.2975 | 0.2880 | 0.2952 |
8 | Heilongjiang | 0.2268 | 0.2433 | 0.2446 | 0.2480 | 0.2762 | 0.2776 | 0.2943 | 0.3135 | 0.3138 | 0.3262 | 0.3267 |
9 | Shanghai | 0.1769 | 0.1849 | 0.1972 | 0.2046 | 0.2049 | 0.2122 | 0.2221 | 0.3196 | 0.3347 | 0.3407 | 0.3550 |
10 | Jiangsu | 0.2933 | 0.3114 | 0.3293 | 0.3304 | 0.3493 | 0.3605 | 0.3769 | 0.3997 | 0.4388 | 0.4240 | 0.4601 |
11 | Zhejiang | 0.3016 | 0.3112 | 0.3246 | 0.3406 | 0.3505 | 0.3643 | 0.3824 | 0.4096 | 0.4259 | 0.4382 | 0.4507 |
12 | Anhui | 0.2435 | 0.2538 | 0.2663 | 0.2680 | 0.2949 | 0.3100 | 0.3356 | 0.3657 | 0.3787 | 0.3787 | 0.3831 |
13 | Fujian | 0.2318 | 0.2410 | 0.2573 | 0.2784 | 0.2889 | 0.3074 | 0.3234 | 0.3585 | 0.3783 | 0.3770 | 0.3825 |
14 | Jiangxi | 0.2170 | 0.2275 | 0.2429 | 0.2586 | 0.2768 | 0.2891 | 0.3056 | 0.3270 | 0.3431 | 0.3509 | 0.3673 |
15 | Shandong | 0.3261 | 0.3436 | 0.3616 | 0.3613 | 0.3821 | 0.3992 | 0.4078 | 0.4363 | 0.4496 | 0.4454 | 0.4485 |
16 | Henan | 0.2484 | 0.2584 | 0.2691 | 0.2795 | 0.2979 | 0.2998 | 0.3112 | 0.3394 | 0.4103 | 0.3620 | 0.3953 |
17 | Hebei | 0.2533 | 0.2668 | 0.2828 | 0.2840 | 0.3062 | 0.3155 | 0.3280 | 0.3349 | 0.3518 | 0.3585 | 0.3801 |
18 | Hunan | 0.2424 | 0.2549 | 0.2683 | 0.2805 | 0.3009 | 0.3085 | 0.3197 | 0.3391 | 0.3692 | 0.3936 | 0.3818 |
19 | Guangdong | 0.2631 | 0.2802 | 0.2931 | 0.3087 | 0.3250 | 0.3397 | 0.3518 | 0.3749 | 0.3886 | 0.3827 | 0.3811 |
20 | Guangxi | 0.2113 | 0.2223 | 0.2362 | 0.2579 | 0.2695 | 0.2880 | 0.3074 | 0.3475 | 0.3735 | 0.3825 | 0.3953 |
21 | Hainan | 0.1875 | 0.1920 | 0.2130 | 0.2284 | 0.2446 | 0.2504 | 0.2551 | 0.2642 | 0.2702 | 0.2996 | 0.3228 |
22 | Chongqing | 0.1936 | 0.2096 | 0.2267 | 0.2360 | 0.2610 | 0.2734 | 0.2981 | 0.3205 | 0.3631 | 0.3623 | 0.3839 |
23 | Sichuan | 0.2472 | 0.2617 | 0.2771 | 0.2812 | 0.3022 | 0.3138 | 0.3286 | 0.3580 | 0.3852 | 0.3762 | 0.3787 |
24 | Guizhou | 0.1541 | 0.1683 | 0.1907 | 0.2099 | 0.2256 | 0.2667 | 0.2933 | 0.3324 | 0.3589 | 0.3428 | 0.3534 |
25 | Yunnan | 0.2096 | 0.2143 | 0.2303 | 0.2448 | 0.2590 | 0.2821 | 0.2941 | 0.3272 | 0.3676 | 0.3492 | 0.3711 |
26 | Shaanxi | 0.2005 | 0.2150 | 0.2365 | 0.2491 | 0.2571 | 0.2751 | 0.2929 | 0.3096 | 0.3363 | 0.3278 | 0.3402 |
27 | Gansu | 0.1422 | 0.1562 | 0.1641 | 0.1743 | 0.1939 | 0.2013 | 0.2123 | 0.2459 | 0.2516 | 0.2727 | 0.2904 |
28 | Qinghai | 0.0834 | 0.0822 | 0.0960 | 0.1184 | 0.1280 | 0.1485 | 0.1555 | 0.1950 | 0.2349 | 0.2330 | 0.2466 |
29 | Ningxia | 0.0665 | 0.0689 | 0.0862 | 0.1032 | 0.1078 | 0.1256 | 0.1396 | 0.1859 | 0.2625 | 0.2936 | 0.3066 |
30 | Xinjiang | 0.1538 | 0.1595 | 0.1722 | 0.1824 | 0.1913 | 0.2098 | 0.2135 | 0.2557 | 0.2837 | 0.2987 | 0.3200 |
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Type of Variables | Evaluation of Indicator | Unit | |
---|---|---|---|
Input indicators | Agricultural Labor Input | Number of employees in the primary industry | 10,000 persons |
Agricultural Land Input | Total sown area of crops and aquaculture area | 1000 hectares | |
Agricultural Capital Input | Usage of agricultural fertilizers | 10,000 tons | |
Total power of agricultural machinery | 10,000 kilowatts | ||
Usage of agricultural plastic film | 10,000 tons | ||
Usage of pesticides | 10,000 tons | ||
Agricultural Energy Input | Usage of agricultural diesel | 10,000 tons | |
Agricultural Water Input | Effective irrigation area | 1000 hectares | |
Output indicators | Desired Output | Gross output value of agriculture, forestry, animal husbandry, and fishery | 100 million CNY |
Undesired Output | Agricultural carbon emissions | 10,000 tons |
Elements | Indicators | Attribute | Weight (%) |
---|---|---|---|
Agriculture | Gross output value of agriculture, forestry, animal husbandry, and fishery (10,000 CNY) | + | 11.40 |
Proportion of typical counties for rural entrepreneurship and innovation (number of typical counties for rural entrepreneurship and innovation/total number of counties in the region) | + | 53.66 | |
Number of green food enterprises (number of certified green food units in the current year) | + | 17.16 | |
Forest coverage rate | + | 8.42 | |
Land productivity (gross agricultural output value/sown area of crops) | + | 9.36 | |
Tourism | Number of A-level scenic spots | + | 18.69 |
Proportion of demonstration counties for leisure agriculture (number of demonstration counties for leisure agriculture/total number of counties in the region) | + | 20.44 | |
Total tourism revenue (100 million CNY) | + | 25.60 | |
Total number of tourist arrivals (10,000 persons) | + | 18.05 | |
Number of travel agencies (units) | + | 17.22 |
Variable Type | Variable Name | Calculation Method | Data Source |
---|---|---|---|
Explained variable | Agricultural green total factor productivity (AGTFP) | Measured and evaluated using the SBM-GML index | By calculation |
Core explanatory variable | Agriculture and tourism integration (ATI) | Calculated using the coupling coordination degree model | By calculation |
Mediating variables | Agricultural industrial structure adjustment index (AISAI) | 1—(Agricultural output value/Gross output value of agriculture, forestry, animal husbandry, and fishery) | CSY |
Threshold variables | Human capital level (HCL) | Number of students enrolled in higher education institutions/total population | CSY |
Control variables | Disaster severity (DS) | Proportion of disaster-affected area in total sown area of crops | MARA, CSY |
Informatization level (IL) | Total volume of post and telecommunications business/regional gross production value | CSY | |
Trade dependence (TD) | Total import and export of agricultural products value/gross production value | MARA |
Variables | Mean | Median | Max | Min | Std. Dev | Observations |
---|---|---|---|---|---|---|
AGTFP | 1.3741 | 1.2591 | 3.1306 | 0.6494 | 0.3964 | 330 |
ATI | 0.2819 | 0.2838 | 0.4601 | 0.0665 | 0.0775 | 330 |
AISAI | 0.4776 | 0.4956 | 0.6489 | 0.0940 | 0.0912 | 330 |
DS | 0.1567 | 0.1279 | 0.6955 | 0.0021 | 0.1181 | 330 |
HCL | 0.0213 | 0.0208 | 0.0436 | 0.0085 | 0.0057 | 330 |
IL | 0.0697 | 0.0383 | 2.5129 | 0.0147 | 0.1456 | 330 |
TD | 0.0257 | 0.0129 | 0.1082 | 0.0007 | 0.0202 | 330 |
Variables | AGTFP | ATI | AISAI | DS | HCL | IL | TD | VIF |
---|---|---|---|---|---|---|---|---|
AGTFP | 1.0000 | |||||||
ATI | 0.5487 *** | 1.0000 | 1.4697 | |||||
AISAI | 0.1084 ** | 0.2391 *** | 1.0000 | 1.2969 | ||||
DS | −0.2639 *** | −0.3281 *** | −0.0914 * | 1.0000 | 1.2346 | |||
HCL | 0.4711 *** | 0.4456 *** | 0.0307 | −0.3852 *** | 1.0000 | 1.4032 | ||
IL | 0.0923 * | 0.1467 *** | −0.0827 | −0.0422 | −0.0578 | 1.0000 | 1.0758 | |
TD | −0.1940 *** | 0.2717 *** | 0.2920 *** | −0.3084 *** | 0.1610 *** | −0.0825 | 1.0000 | 1.1532 |
Explained Variable: lnAGTFP | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
ATI | 0.9232 *** (0.0330) | 0.8474 *** (0.0361) | 0.4369 *** (0.0462) | 0.5206 *** (0.0481) | 0.5009 *** (0.0476) |
DS | −0.0439 *** (0.0097) | −0.0269 *** (0.0082) | −0.0240 *** (0.0079) | −0.0225 *** (0.0078) | |
HCL | 0.8182 *** (0.0702) | 0.7507 *** (0.0693) | 0.6929 *** (0.0702) | ||
IL | −0.0403 *** (0.0086) | −0.0349 *** (0.0086) | |||
TD | −0.0863 *** (0.0254) | ||||
Constant | 1.4939 *** (0.0439) | 1.6187 *** (0.0507) | 4.1721 *** (0.2229) | 3.8821 *** (0.2242) | 3.6564 *** (0.2301) |
Province FE | YES | YES | YES | YES | YES |
Observations | 330 | 330 | 330 | 330 | 330 |
R2 | 0.7599 | 0.7753 | 0.8459 | 0.8565 | 0.8619 |
Baseline Model | Mediation Effect | ||
---|---|---|---|
Explained Variable: | AGTFP (5) | AISAI (6) | AGTFP (7) |
ATI | 0.5009 *** (0.0476) | 0.1577 ** (0.0665) | 0.1066 *** (0.0328) |
AISAI | 0.0713 ** (0.0336) | ||
Control variables | YES | YES | YES |
Constant | 3.6564 *** (0.2301) | −1.2334 *** (0.3642) | 0.9277 *** (0.1454) |
Province FE | YES | YES | YES |
R2 | 0.8619 | 0.7933 | 0.7863 |
Observations | 330 | 330 | 330 |
Mediation effect | Partial | ||
Proportion of effect | 2.24% |
Threshold Variable (q) | Threshold | F-Statistic | Critical Value | Bootstrap Repeat | ||
---|---|---|---|---|---|---|
1% | 5% | 10% | ||||
ATI | Single | 16.19 | 37.3564 | 27.3414 | 21.6812 | 500 |
Double | 21.16 ** | 22.9184 | 17.2474 | 14.4312 | 500 | |
Triple | 7.80 | 43.2602 | 23.6974 | 17.1407 | 500 | |
HCL | Single | 42.04 ** | 50.5502 | 36.4689 | 32.0932 | 500 |
Double | 19.24 | 51.5092 | 36.6339 | 30.1454 | 500 |
Threshold Variable (q) | Threshold | Estimated Threshold Value | 95% Confidence Interval |
---|---|---|---|
ATI | Single | −1.1673 | [−1.1880, −1.1672] |
Double | −0.8580 | [−0.8866, −0.8535] | |
HCL | Single | −3.8925 | [−3.8965, −3.8913] |
(8) | (9) | |
---|---|---|
Threshold Variable (q) | ||
Explained Variable: lnAGTFP | ATI | HCL |
) | 0.4875 *** (0.0739) | |
) | 0.4181 *** (0.0737) | |
) | 0.2392 * (0.1189) | |
) | 0.7783 *** (0.0955) | |
) | 0.6792 *** (0.1052) | |
Control variables | YES | YES |
Constant | 3.1795 *** (0.4001) | 1.2801 *** (0.1249) |
Province FE | YES | YES |
Observations | 330 | 330 |
R2 | 0.8542 | 0.8128 |
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Share and Cite
Yang, B.; Li, Y.; Wang, M.; Liu, J. Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China. Agriculture 2024, 14, 1386. https://doi.org/10.3390/agriculture14081386
Yang B, Li Y, Wang M, Liu J. Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China. Agriculture. 2024; 14(8):1386. https://doi.org/10.3390/agriculture14081386
Chicago/Turabian StyleYang, Bing, Yansong Li, Mengjiao Wang, and Jianxu Liu. 2024. "Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China" Agriculture 14, no. 8: 1386. https://doi.org/10.3390/agriculture14081386
APA StyleYang, B., Li, Y., Wang, M., & Liu, J. (2024). Nonlinear Nexus between Agricultural Tourism Integration and Agricultural Green Total Factor Productivity in China. Agriculture, 14(8), 1386. https://doi.org/10.3390/agriculture14081386