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Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting

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Today, one of the biggest challenges facing the UK is the new target set when the nation became first major economy to pass net zero emissions law, which requires the country to bring all greenhouse gas emissions to net zero by 2050. On the one hand, there are already a few ideas about how we should farm and use land in order to deliver such a target. On the other hand, the government has a new strategy which is to pay farmers for providing public goods, especially for climate change mitigation through the reduction and storage of greenhouse gas emissions. The most critical task is to find a solution to such a question as \How should public spending on farm public goods be allocated?" In this paper, we argue that formulating an effective subsidy scheme cannot focus on the public need alone, but should also take into consideration what farmers must endure and the opportunities they must forgo. This requires a good understanding about the generating process behind the spatial heterogeneity of agricultural land-use at a _ne spatial scale. We aim to provide government and its agents with decision support for policy making post-Brexit in two directions. Firstly, we employ detailed spatial resolution data and establish a new statistical tool that can help: (i) to effectively capture the spatial heterogeneity of agricultural land-use, (ii) to disentangle the contributions of terrain formulations, environmental characteristics, climatic conditions, policies, and other legacy and agglomeration effects in the generating process of the land-use patterns, and (iii) accurately gauge their relative importance across different regions of GB for more targeted subsidies schemes. Secondly, we employ our new method and provide policy advice and evaluation.

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  • W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
  • Handle: RePEc:cdf:wpaper:2021/8
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Man Li & JunJie Wu & Xiangzheng Deng, 2013. "Identifying Drivers of Land Use Change in China: A Spatial Multinomial Logit Model Analysis," Land Economics, University of Wisconsin Press, vol. 89(4), pages 632-654.
    3. Badi H. Baltagi, 2015. "Seemingly Unrelated Regressions," Springer Texts in Business and Economics, in: Solutions Manual for Econometrics, edition 3, chapter 0, pages 233-257, Springer.
    4. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    5. Carlo Fezzi & Ian J. Bateman, 2011. "Structural Agricultural Land Use Modeling for Spatial Agro-Environmental Policy Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 1168-1188.
    6. Shew Fan Liu & Zhenlin Yang, 2015. "Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model," Econometrics, MDPI, vol. 3(2), pages 1-36, May.
    7. Shannon M. Sterling & Agnès Ducharne & Jan Polcher, 2013. "The impact of global land-cover change on the terrestrial water cycle," Nature Climate Change, Nature, vol. 3(4), pages 385-390, April.
    8. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2017. "Aggregated Versus Individual Land-Use Models: Modeling Spatial Autocorrelation to Increase Predictive Accuracy," Post-Print hal-01868560, HAL.
    9. Steven T. Yen & Biing-Hwan Lin & David M. Smallwood, 2003. "Quasi- and Simulated-Likelihood Approaches to Censored Demand Systems: Food Consumption by Food Stamp Recipients in the United States," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 458-478.
    10. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    11. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    12. Anne Lacroix & Alban Thomas, 2011. "Estimating the Environmental Impact of Land and Production Decisions with Multivariate Selection Rules and Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 780-798.
    13. Carmen Carrión-Flores & Elena G. Irwin, 2004. "Determinants of Residential Land-Use Conversion and Sprawl at the Rural-Urban Fringe," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 889-904.
    14. Robert G. Chambers & Richard E. Just, 1989. "Estimating Multioutput Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(4), pages 980-995.
    15. Gerald C. Nelson & Daniel Hellerstein, 1997. "Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 80-88.
    16. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    17. Badi H. Baltagi, 2021. "Seemingly Unrelated Regressions with Error Components," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 149-155, Springer.
    18. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    19. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    20. Moscone, Francesco & Knapp, Martin & Tosetti, Elisa, 2007. "Mental health expenditure in England: A spatial panel approach," Journal of Health Economics, Elsevier, vol. 26(4), pages 842-864, July.
    21. David E. Sahn, 2005. "Consistent Estimation of Censored Demand Systems Using Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 660-672.
    22. Wooldridge, Jeffrey M., 1995. "Selection corrections for panel data models under conditional mean independence assumptions," Journal of Econometrics, Elsevier, vol. 68(1), pages 115-132, July.
    23. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    24. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    25. Diansheng Dong & Brian W. Gould & Harry M. Kaiser, 2004. "Food Demand in Mexico: An Application of the Amemiya-Tobin Approach to the Estimation of a Censored Food System," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1094-1107.
    26. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    27. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
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    More about this item

    Keywords

    Agro-environmental policy; land-use; multivariate Tobit; system of censored equation; spatial model; error component model.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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