The Relationship between Land Transfer and Agricultural Green Production: A Collaborative Test Based on Theory and Data
Abstract
:1. Introduction
2. Theoretical Analysis
3. Materials and Methods
3.1. Data
3.2. Variables
3.2.1. Explained Variables: Green Production in Agriculture (GPA)
3.2.2. Explanatory Variables: Land Transfer (LT)
3.2.3. Control Variables (CV)
3.3. Method
4. Results
4.1. Basic Regression
4.2. Endogenous Issue Treat
4.3. Robustness Test
4.4. Heterogeneity Test
4.4.1. Heterogeneity Analysis for AEP and RE
4.4.2. Heterogeneity Analysis for AI and AD
5. Further Research: Moderating Effect Test
5.1. Testing for Agricultural Training Moderation Effects
5.2. Testing for Digital Literacy Moderation Effects
5.3. Testing for Agricultural Loans Moderation Effects
6. Conclusions, Discussion and Policy Recommendation
6.1. Conclusions
6.2. Discussion
6.3. Policy Recommendation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables Type | Variables | Define |
---|---|---|
Explained variables. | Green production in agriculture (GPA) | According to “Has your household’s fertilizer (B09), pesticides (B11), mulches (B14) and mechanical fuel (B23) use in agricultural production increased, decreased or remained more or less the same in the last 3 years? a = increased = −1, b = remained unchanged = 0, c = decreased = 1; What are the main methods of disposal of waste mulch in your household? (B13) a = buried in situ = −1, b = open burning = −1, c = harmless disposal = 1; What are the main methods in which your household handles straw in farming and livestock production? (B15) a = straw return to the field (land) = 1, b = open burning = −1, c = for domestic indoor fuel = −1, d = processed as livestock feed = 1, e = processing and cultivation of edible mushrooms = 1; Has the amount of biological manure (green fertilizer) used in your household increased, decreased or remained more or less the same in the last 3 years? (B18) a = increased = 1, b = remained unchanged = 0, c = decreased = −1”. Using a subjective weighting method, we selected data from 7 questions and set “1 = green production, 0 and −1 = non-green production”, then the green production index was calculated using principal component analysis and factor analysis models to measure the level of agricultural green production. |
Green production in agriculture (GPA_2) | According to “Does your household use chemical fertilizers (B08), pesticides (B10), mulches (B12), mechanical fuel (B22) in agricultural production? a = Yes, b = No; Does your household use green manure in agricultural production? (B17) a = Yes, b = No; Has your household expanded agricultural land area by clearing woodland or grassland? (B27) a = Yes, b = No.” Reassign options of B08, B10, B12, B22 and B27 to “b = green production behavior = 1, a = non-green production behavior = 0” and option B17 to “a = green production behavior = 1, b = non-green production behavior = 0”; Then sum up each household’s green production behavior score for each household to measure AGPB. | |
Explanatory variables | Land rent in (LRI) | Does your household rent in land according to the land transfer policy? 1 = Yes, 0 = No. |
Land rent out (LRO) | Does your household rent out land according to the land transfer policy? 1 = Yes, 0 = No. | |
Control variables (CV) | Age | How old are you? (Unit: years) |
Gender | 1 = Male; 2 = Female. | |
Marriage | 1 = Unmarried; 2 = Married; 3 = Divorced; 4 = Death of wife/husband. | |
Education | Respondents’ educational level is: 1 = Illiterate; 2 = Primary school; 3 = Junior high school; 4 = High/vocational school; 5 = Undergraduate/polytechnic; 6 = Master/doctor. | |
Health | What do you think of your health? 1 = very poor; 2 = poor; 3 = fair; 4 = good; 5 = very good. | |
Social capital (SC) | How many good friends do you have in your local area? 1 = 0, 2 = 1~3, 3 = 4~7, 4 = 7+ | |
Number of agricultural labors (AL) | How many people in your household are permanently involved in agricultural or pastoral production? | |
Agricultural subsidy (AS) | How much was your household’s agricultural subsidy last year in approximately RMB? (Agricultural subsidy). Ln (agricultural subsidy). | |
Experiences of work outside (EWO) | Have you worked outside the home in the last 3 years? 1 = Yes; 0 = No | |
Non-agricultural production income (NAPI) | What was your household’s total income last year in approximately RMB? (Total household income); What was your household’s agricultural production income last year in approximately RMB? (Agricultural production income). Ln (Total household income-Agricultural production income) | |
Household energy use (HEU) | First, according to the questions “How often does your household use firewood/grass/straw (C14), coal (C15), gas (C16), cow dung (C17), biogas (C18), natural gas (C19), LPG (C20), electricity (C21) and solar energy (C22) in your daily life (cooking/heating/bathing)? a = never use = 1, b = hardly ever use = 2, c = occasionally use = 3, d = often use = 4, e = daily use = 5”; Then, the frequency scores for “C14-C17” were summed and set as the frequency of non-clean energy(FNCE) use, and the frequency scores for “C18-C22” were summed and set as the frequency of clean energy(FCE) use; Finally, FNCE and FCE were compared and if FCE < FNCE, assign a value of “0”, if FCE = FNCE, assign a value of “1”, if FCE > FNCE, assign a value of “2”. | |
Non-agricultural economics | Does your village develop non-agricultural economic industries? 1 = yes, 0 = no. |
Variable | Observations | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
GPA | 454 | 0 | 0.522 | −1.123 | 0.779 |
GPA_2 | 454 | 2.434 | 1.054 | 1 | 6 |
LRI | 454 | 0.269 | 0.444 | 0 | 1 |
LRO | 454 | 0.361 | 0.481 | 0 | 1 |
Age | 454 | 42.33 | 8.599 | 18 | 68 |
Gender | 454 | 0.722 | 0.448 | 0 | 1 |
Marriage | 454 | 2.04 | 0.513 | 1 | 4 |
Education | 454 | 2.771 | 1.003 | 1 | 6 |
Health | 454 | 4.02 | 0.558 | 1 | 5 |
SC | 454 | 2.892 | 0.802 | 1 | 4 |
AL | 454 | 2.126 | 0.619 | 1 | 5 |
AS | 454 | 6.985 | 1.686 | 0 | 8.7 |
EWO | 454 | 0.652 | 0.477 | 0 | 1 |
NAPI | 454 | 11.563 | 0.596 | 9.9 | 14.22 |
HEU | 454 | 1.729 | 0.63 | 0 | 2 |
NAE | 454 | 0.381 | 0.486 | 0 | 1 |
(1) | (1) | (1) | (1) | |
---|---|---|---|---|
Variables | GPA | GPA | GPA | GPA |
LRI | 0.166 *** | 0.223 *** | ||
(0.0530) | (0.0566) | |||
LRO | −0.0766 | −0.0546 | ||
(0.0493) | (0.0537) | |||
Age | −0.0162 *** | −0.0139 *** | ||
(0.00415) | (0.00419) | |||
Gender | −0.0876 * | −0.0940 * | ||
(0.0525) | (0.0532) | |||
Marriage | −0.0843 * | −0.0674 | ||
(0.0503) | (0.0501) | |||
Education | −0.0577 * | −0.0425 | ||
(0.0304) | (0.0305) | |||
Health | −0.00182 | −0.0159 | ||
(0.0380) | (0.0377) | |||
SC | 0.0651 ** | 0.0683 ** | ||
(0.0296) | (0.0305) | |||
AL | 0.0778 ** | 0.0526 | ||
(0.0349) | (0.0351) | |||
AS | −0.0193 * | −0.0170 | ||
(0.0107) | (0.0114) | |||
EWO | 0.0974 * | 0.119 ** | ||
(0.0524) | (0.0532) | |||
NAPI | 0.0998 *** | 0.124 *** | ||
(0.0383) | (0.0407) | |||
HEU | 0.0936 *** | 0.0947 *** | ||
(0.0341) | (0.0354) | |||
NAE | −0.0522 | −0.0659 | ||
(0.0487) | (0.0493) | |||
Constant | −0.0447 | −0.549 | 0.0277 | −0.850 * |
(0.0289) | (0.478) | (0.0318) | (0.496) | |
Observations | 454 | 454 | 454 | 454 |
R-squared | 0.20 | 0.153 | 0.11 | 0.125 |
IV = COVID_19 | IV = Distance | |||||
---|---|---|---|---|---|---|
First Stage | Second Stage | First Stage | Second Stage | |||
Probit (1) | OLS (2) | 2SLS (3) | Probit (4) | OLS (5) | 2SLS (6) | |
Variables | LRI | GPA | GPA | LRI | GPA | GPA |
LRI | 0.223 *** | 0.195 *** | 0.223 *** | 0.187 *** | ||
(0.0566) | (0.0517) | (0.0566) | (0.0624) | |||
COVID_19 | 0.377 *** | 0.0251 | ||||
(0.133) | (0.0199) | |||||
Distance | −0.353 *** | 0.256 | ||||
(0.0689) | (0.137) | |||||
CV | Control | Control | Control | Control | Control | Control |
Constant | −0.744 *** | 0.055 ** | 0.0547 ** | 0.570 ** | 0.451 | 0.0714 * |
(0.0787) | (0.0241) | (0.0229) | (0.237) | (0.255) | (0.0321) | |
F-value | 69.21 | 102.33 | ||||
R-squared | 0.184 | 0.210 | ||||
Observations | 454 | 454 | 454 | 454 | 454 | 454 |
OLS (1) | OLS (2) | OLS (3) | OLS (4) | |
---|---|---|---|---|
Variables | GPA_2 | GPA_2 | GPA_2 | GPA_2 |
LRI | 0.236 ** | 0.277 *** | ||
(0.119) | (0.104) | |||
LRO | −0.469 | −0.349 | ||
(0.292) | (0.267) | |||
CV | Control | Control | ||
Constant | 2.370 *** | 8.403 *** | 0.603 | 0.953 |
(0.0553) | (1.231) | (0. 466) | (0.621) | |
Observations | 454 | 454 | 454 | 454 |
R-squared | 0.212 | 0.161 | 0.119 | 0.170 |
OLS (1) | OLS (2) | OLS (3) | OLS (4) | |
---|---|---|---|---|
Variables | AEP = 1 | AEP = 0 | RE = 1 | RE = 0 |
GPA | GPA | GPA | GPA | |
LRI | 0.196 *** | −0.0243 | 0.305 ** | 0.136 ** |
(0.0572) | (0.119) | (0.123) | (0.0587) | |
CV | Control | Control | Control | Control |
Constant | 0.00300 | −0.128 ** | −0.0866 | −0.0331 |
(0.0350) | (0.0499) | (0.0630) | (0.0325) | |
R-squared | 0.134 | 0.091 | 0.156 | 0.140 |
Observations | 306 | 148 | 92 | 362 |
OLS (1) | OLS (2) | OLS (3) | OLS (4) | |
---|---|---|---|---|
Variables | AI = 1 | AI = 0 | AD = 1 | AD = 0 |
GPA | GPA | GPA | GPA | |
LRI | 0.350 *** | 0.136 ** | −0.109 | 0.200 *** |
(0.0671) | (0.0656) | (0.158) | (0.0568) | |
CV | Control | Control | Control | Control |
Constant | −0.0268 | −0.0582 | 0.0545 | −0.0679 ** |
(0.0416) | (0.0399) | (0.0553) | (0.0331) | |
R-squared | 0.160 | 0.141 | 0.132 | 0.192 |
Observations | 165 | 289 | 70 | 384 |
OLS (1) | OLS (2) | OLS (3) | OLS (4) | OLS (5) | OLS (6) | OLS (7) | |
---|---|---|---|---|---|---|---|
GPA | GPA | GPA | GPA | GPA | GPA | GPA | |
LRI | 0.223 *** | 0.155 *** | 0.149 *** | 0.207 *** | 0.194 *** | 0.201 *** | 0.518 ** |
(0.0566) | (0.0536) | (0.0521) | (0.0521) | (0.0734) | (0.0541) | (0.249) | |
AT | 0.111 ** | 0.248 *** | |||||
(0.0562) | (0.0652) | ||||||
LRI × AT | 0.197 *** | ||||||
(0.0594) | |||||||
DL | 0.205 *** | 0.196 *** | |||||
(0.0478) | (0.0576) | ||||||
LRI × DL | 0.301 *** | ||||||
(0.0640) | |||||||
AL | 0.0843 ** | 0.0483 ** | |||||
(0.0383) | (0.0208) | ||||||
LRI × AL | 0.114 *** | ||||||
(0.0265) | |||||||
CV | Control | Control | Control | Control | Control | Control | Control |
Constant | 0.109 ** | −0.0394 | −0.0844 ** | −0.0278 | −0.0900 | −0.0561 * | |
(0.0468) | (0.0273) | (0.0357) | (0.0259) | (0.0832) | (0.0287) | ||
R-squared | 0.153 | 0.109 | 0.216 | 0.197 | 0.129 | 0.209 | 0.199 |
Observations | 454 | 454 | 454 | 454 | 454 | 454 | 454 |
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Zang, D.; Yang, S.; Li, F. The Relationship between Land Transfer and Agricultural Green Production: A Collaborative Test Based on Theory and Data. Agriculture 2022, 12, 1824. https://doi.org/10.3390/agriculture12111824
Zang D, Yang S, Li F. The Relationship between Land Transfer and Agricultural Green Production: A Collaborative Test Based on Theory and Data. Agriculture. 2022; 12(11):1824. https://doi.org/10.3390/agriculture12111824
Chicago/Turabian StyleZang, Dungang, Sen Yang, and Fanghua Li. 2022. "The Relationship between Land Transfer and Agricultural Green Production: A Collaborative Test Based on Theory and Data" Agriculture 12, no. 11: 1824. https://doi.org/10.3390/agriculture12111824
APA StyleZang, D., Yang, S., & Li, F. (2022). The Relationship between Land Transfer and Agricultural Green Production: A Collaborative Test Based on Theory and Data. Agriculture, 12(11), 1824. https://doi.org/10.3390/agriculture12111824