Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions
Abstract
:1. Introduction
2. Data and Method
2.1. Land Cover Data
2.2. Explanatory Variables
2.2.1. Spatial Planning
2.2.2. Decentralisation
Variable | Description | Data Source | Mean | Standard Dev. | Min | Max |
---|---|---|---|---|---|---|
Dependent variable | ||||||
LAND_CONV | Area of agricultural land converted to urban use (Ha) | Corine Land Cover (EEA [24]) | 15,587 | 14,825 | 48 | 101,665 |
Independent variable | ||||||
Economic factors | ||||||
AGRI_RENT | Agricultural GVA (million €) per agricultural land (Ha) in a region | Eurostat [86] | 0.0018 | 0.0036 | 0.0001 | 0.0036 |
URBAN_RENT | Industrial and services sector GVA (million €) per urban land (Ha) in a region | Eurostat [86] | 0.289 | 0.248 | 0.0094 | 2.216 |
CAP_LABOUR | Agricultural capital to labour ratio | Eurostat [86] | 0.0903 | 1.351 | 0.0001 | 22 |
G_GVA_AGRI | Growth rate of agricultural GVA between 2000 and 2018 | Eurostat [86] | 20.56 | 55.07 | −94.1 | 392.2 |
G_GVA_IND | Growth rate of industrial/services sector GVA between 2000 and 2018 | Eurostat [86] | 101.23 | 92.46 | −63.9 | 544.2 |
INCOME_CAP | Income to CAP (Common Agricultural Policy) subsidies ratio | Eurostat [86] | 1.908 | 3.278 | 0.068 | 43.949 |
Population factors | ||||||
POP | Population (thousand) | Eurostat [86] | 1.810 | 1.443 | 25.7 | 11,000 |
G_POP | Population growth rate between 2000 and 2018 | Eurostat [86] | 5.091 | 10.354 | −30.08 | 42.78 |
Land use factors | ||||||
AGRI_LAND | Area of agricultural land (Ha) including all the agricultural activities | Corine Land Cover (EEA [24]) | 755,165 | 778,395 | 385 | 4,911,564 |
URBAN_LAND | Area of residential, industrial/commercial, and recreational land (Ha) | Corine Land Cover (EEA [24]) | 78,607 | 52,816 | 842 | 299,184 |
Planning systems | ||||||
STATE_LED | Dummy equal to 1 if there is state-led system | Berisha et al. [60] | 0.294 | 0.456 | 0 | 1 |
MARKET_LED | Dummy equal to 1 if there is market-led neo-performative system | Berisha et al. [60] | 0.286 | 0.453 | 0 | 1 |
CONFORM | Dummy equal to 1 if there is conformative system | Berisha et al. [60] | 0.351 | 0.478 | 0 | 1 |
Base category (Misled performative system) | Berisha et al. [60] | |||||
CONFORM_PERFORM | An index where lower values show conformative planning and higher values performative planning | Berisha et al. [60] | 2.716 | 0.711 | 2 | 4 |
MARKET_STATE | An index where lower values show market-led development and higher values show state-led development | Berisha et al. [60] | −0.613 | 1.199 | −2.5 | 1.5 |
Decentralisation | ||||||
FED_COUNT | Dummy equal to 1 if federal or quasi federal country | 0.283 | 0.451 | 0 | 1 | |
Base category (unitary countries) | OECD [87] | |||||
TIER | The number of government levels in a country | Treisman [79] | 3.592 | 0.529 | 2 | 4 |
RAI | Regional autonomy index summarising different dimensions of governmental decentralisation | Hooghe et al. [78] | 18.818 | 11.057 | 1 | 35 |
Corruption in public sector | ||||||
CPI | Corruption Perceptions Index measuring the perceived levels of public sector corruption | downloaded 12 July 2022 from: www.icgg.org | 6.466 | 1.781 | 3.5 | 9.3 |
Regularity quality and government effectiveness | ||||||
REG_Q | Ability of the government to formulate and implement sound policies and regulations that promote private sector development | Kaufmann et al. [88] | 85.736 | 9.983 | 66.34 | 99.03 |
GOV_EFFECT | The quality of public services, policy formation and implementation and the credibility of the government’s commitment to such policies | Kaufmann et al. [88] | 82.831 | 12.317 | 43.27 | 99.04 |
Institutional fragmentation | ||||||
MUNICIP | Number of municipalities in a NUTS region | Eurostat [89] | 452.93 | 538.59 | 1 | 3020 |
Land value capturing | ||||||
IMPACT_FEE | Dummy equal to 1 if impact fees are paid by landowners for the construction of infrastructure | OECD [87] | 0.516 | 0.5 | 0 | 1 |
JOINT_DEV | Dummy equal to 1 if public bodies and private developers develop land jointly and share the profit | OECD [87] | 0.169 | 0.376 | 0 | 1 |
PROP_TAX | Dummy equal to 1 if landowners pay property or land value taxes | OECD [87] | 0.064 | 0.245 | 0 | 1 |
LAND_BANK | Dummy equal to 1 if land banks assemble small plots for further development or sale | OECD [87] | 0.26 | 0.439 | 0 | 1 |
TAX_INC | Dummy equal to 1 if investments are financed by borrowing against expected increases in future tax revenues | OECD [87] | 0.166 | 0.373 | 0 | 1 |
BET_LEVY | Dummy equal to 1 if increase in property values due to a public action (e.g., Re-zoning, infrastructure investment) is captured | OECD [87] | 0.061 | 0.239 | 0 | 1 |
Base category (no value capture) | OECD [87] | |||||
Spatial policy integration | ||||||
NAT_POL | An index showing the degree of integration of the agriculture, rural and environmental policies at the national level | ESPON [74] | 2.807 | 1.151 | 1 | 4.5 |
SUB_NAT_POL | An index showing the degree of integration of the agriculture, rural and environmental policies at the sub-national level | ESPON [74] | 3.662 | 0.795 | 1 | 5 |
LOC_POL | An index showing the degree of integration of the agriculture, rural and environmental policies at the local level | ESPON [74] | 3.883 | 0.755 | 1 | 5 |
2.2.3. Corruption in Public Sector
2.2.4. Regularity Quality and Government Effectiveness
2.2.5. Institutional Fragmentation
2.2.6. Spatial Policy Integration
2.2.7. Other Explanatory Variables
2.3. Regression Methods
3. Results
3.1. Main Findings
3.2. Results from OLS Regression Models
3.3. Results from MGWR Models
4. Discussion and Findings
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
1 | In this research, land use change is a process by which human activities transform the actual land use to other land uses, referring to how land has been used, emphasising the functional role of land for economic activities. Land use change and landscape change can be interchangeably used throughout the text. |
2 | Regarding the respective positions between state-led and market-led models, Berisha et al. [60] classified the scales as follows: 2, spatial development is mainly driven by the state; 1, spatial development is mainly driven by the state and the market, with a prevalence of the former; 0, ideal balance between state and market; −1, spatial development is mainly driven by the state and the market, with a prevalence of the latter; −2, spatial development is mainly driven by the market. |
3 | The index was constructed based on the sub-indices including institutional depth, policy scope, fiscal autonomy, barrowing autonomy, representation, law making, executive control, fiscal control, barrowing control, and constitutional reform. Their details and scoring can be seen in Hooghe et al. [78]. |
4 | Survey sources for the CPI are: (1) the Country Performance Assessment Ratings by the Asian Development Bank; (2) the Country Policy and Institutional Assessment by the African Development Bank; (3) the Bertelsmann Transformation Index; (4) the Country Policy and Institutional Assessment by the IDA and IBRD (World Bank); (5) the Economist Intelligence Unit; (6) Freedom House Nations in Transit; (7) Global Insight Country Risk Ratings; (8) the International Institute for Management Development; (9) Grey Area Dynamics Ratings by the Merchant International Group; (10) the Political and Economic Risk Consultancy, Hong Kong; (11) the World Economic Forum. |
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Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
---|---|---|---|---|---|
Constant | −7.001 ** (1.02) | −4.678 ** (1.07) | 30.81 ** (5.34) | 24.959 ** (4.83) | 23.195 ** (4.82) |
lnPOP | 0.630 ** (0.14) | 0.699 ** (0.14) | 0.445 ** (0.18) | 0.213 * (0.16) | 0.251 * (0.15) |
lnAGRI_RENT | −0.109 (0.08) | −0.096 (0.08) | −0.021 (0.07) | −0.059 (0.07) | −0.006 (0.06) |
lnURBAN_RENT | 0.09 (0.09) | 0.188 ** (0.09) | 0.216 * (0.11) | 0.176 * (0.09) | 0.114 (0.09) |
lnAGRI_LAND | 0.610 ** (0.05) | 0.633 ** (0.05) | 0.654 ** (0.05) | 0.476 ** (0.05) | 0.492 ** (0.05) |
lnURBAN_LAND | −0.187 (0.16) | −0.375 ** (0.15) | −0.142 (0.18) | 0.205 (0.17) | 0.197 (0.16) |
lnCAPIT_LABOUR | 0.015 (0.03) | 0.071 ** (0.03) | 0.001 (0.03) | −0.012 (0.04) | −0.047 (0.03) |
lnINCOME_CAP | −0.06 * (0.04) | −0.092 ** (0.04) | −0.021 (0.04) | −0.08 * (0.04) | −0.081 * (0.04) |
lnG_POP | 0.218 ** (0.13) | 0.212 ** (0.13) | 0.132 (0.12) | 0.191 ** (0.11) | 0.239 ** (0.09) |
lnG_GVA_IND | −0.181 ** (0.07) | −0.190 ** (0.07) | −0.143 * (0.07) | −0.106 * (0.06) | 0.007 (0.06) |
lnG_GVA_AGRI | 0.072 (0.07) | 0.026 (0.06) | 0.032 (0.06) | 0.047 (0.06) | 0.006 (0.05) |
CONFIRM_PERFORM | 0.207 ** (0.06) | 0.204 ** (0.098) | 0.273 * (0.142) | 0.55 ** (0.19) | 2.874 ** (0.42) |
MARKET_STATE | −0.099 ** (0.05) | −0.204 ** (0.11) | −0.288 * (0.15) | 0.186 (0.20) | 0.814 ** (0.21) |
STATE_LED | - | 0.073 (0.33) | 0.338 (0.49) | 0.171 (0.81) | 1.352 ** (0.79) |
CONFIRM | - | −1.071 ** (0.18) | −1.02 ** (0.23) | −1.219 ** (0.55) | −6.73 ** (0.99) |
MARKET_LED | - | −0.096 (0.22) | 0.496 * (0.33) | 1.721 ** (0.59) | 4.267 ** (0.67) |
FED_COUNT | - | - | −0.101 (0.16) | −1.429 ** (0.3) | −0.722 ** (0.35) |
lnTIER | - | - | 1.337 ** (0.31) | 1.353 ** (0.34) | 4.932 ** (0.68) |
lnRAI | - | - | 0.127 (0.09) | 0.074 (0.09) | 0.251 ** (0.09) |
lnCPI | - | - | 1.991 ** (0.45) | 2.805 ** (0.47) | 4.772 ** (0.62) |
lnMUNICIP | - | - | −0.02 (0.03) | 0.055 (0.03) | −0.024 (0.03) |
lnREG_Q | - | - | −8.528 ** (1.41) | −7.674 ** (1.26) | −9.615 ** (1.29) |
lnGOV_EFFECT | - | - | −0.687 (0.67) | −0.747 (0.61) | −2.353 ** (0.62) |
IMPACT_FEE | - | - | - | −0.271 (0.21) | −0.632 ** (0.22) |
JOINT_DEV | - | - | - | −0.195 (0.15) | −0.035 (0.15) |
PROP_TAX | - | - | - | −0.461 * (0.26) | −1.539 ** (0.31) |
LAND_BANK | - | - | - | −1.092 ** (0.31) | −0.229 (0.4) |
TAX_INC | - | - | - | 0.394 (0.25) | 2.261 ** (0.6) |
BETTER_LEVY | - | - | - | 2.163 ** (0.47) | 6.234 ** (0.76) |
lnNAT_POL | - | - | - | - | 1.937 ** (0.35) |
lnSUB_NAT_POL | - | - | - | - | −0.896 ** (0.16) |
lnLOC_POL | - | - | - | - | −1.244 ** (0.28) |
Number of observations | 265 | 265 | 265 | 265 | 265 |
R-square | 0.74 | 0.78 | 0.83 | 0.88 | 0.9 |
Adj R-square | 0.73 | 0.77 | 0.82 | 0.86 | 0.88 |
F-statistic | F(12,252) = 59.91 | F(15,249) = 59.93 | F(22,242) = 53.74 | F(28,236) = 60.1 | F(31,233) = 64.83 |
Root MSE | 0.605 | 0.556 | 0.49 | 0.43 | 0.39 |
Breusch-Pagan Test | Chi2(1) = 27.81 ** | Chi2(1) = 61.7 ** | Chi2(1) = 54.9 ** | Chi2(1) = 45.1 ** | Chi2(1) = 36.9 ** |
VIF’s | Min(1.22) Max (9.8) | Min (1.3) Max(11) | Min(1.8) Max(15) | Min(1.9) Max(45) | Min(2.0) Max(62) |
Ramsey RESET Test | F(3251) = 0.77 | F(3248) = 3.94 ** | F(3242) = 3.23 ** | F(3236) = 3.59 ** | F(3233) = 3.96 ** |
State-Led Systems | Market-Led Neo-Performative Systems | Conformative Systems | Misled Performative Systems | |
---|---|---|---|---|
Variable | Model 6 | Model 7 | Model 8 | Model 9 |
Constant | −9.851 ** (1.97) | 0.193 (1.517) | −8.513 ** (1.51) | −7.186 (7.85) |
lnPOP | 0.639 ** (0.22) | −0.390 * (0.21) | 0.980 ** (0.24) | −0.035 * (0.95) |
lnAGRI_RENT | −0.192 * (0.12) | −0.070 (0.11) | −0.026 (0.16) | −0.177 (0.45) |
lnURBAN_RENT | −0.054(0.14) | 0.616 ** (0.14) | 0.075 (0.17) | −0.478 (0.87) |
lnAGRI_LAND | 0.527 ** (0.06) | 0.444 ** (0.07) | 0.689 ** (0.19) | 0.216 * (0.29) |
lnURBAN_LAND | −0.274 (0.26) | 0.952 ** (0.23) | −0.766 ** (0.16) | 1.279 ** (0.62) |
ln CAPIT_LABOUR | −0.103 * (0.07) | −0.047 (0.04) | −0.124 * (0.06) | −0.018 * (0.18) |
lnINCOME_CAP | 0.422 ** (0.13) | −0.079 * (0.05) | −0.240 ** (0.06) | 0.515(0.38) |
lnG_POP | 1.117 ** (0.29) | −0.304 (0.19) | 0.441 ** (0.18) | 1.831 ** (0.75) |
lnG_GVA_IND | 0.09 * (0.05) | 0.084 (0.08) | 0.382 (0.27) | 0.074 * (0.39) |
lnG_GVA_AGRI | −0.019 (0.11) | −0.141 (0.22) | −0.279 (0.19) | −1.682 ** (0.79) |
Number of observations | 78 | 76 | 93 | 18 |
R-square | 0.85 | 0.88 | 0.82 | 0.98 |
Adj R-square | 0.83 | 0.86 | 0.8 | 0.97 |
F-statistic | F(10, 67) = 38.35 | F(10, 65) = 45.6 | F(10, 82) = 37.8 | F(10, 7) = 57.8 |
Root MSE | 0.383 | 0.333 | 0.59 | 0.25 |
Breusch-Pagan Test | Chi2(1) = 4.70 ** | Chi2(1) = 4.70 ** | Chi2(1) = 2.99 ** | Chi2(1) = 0.53 |
VIFs | Min(1.04) Max(15.9) | Min(1.73) Max(14.1) | Min(1.73) Max(15.3) | Min(2.35) Max(57.3) |
Ramsey RESET Test | F(3.64) = 5.68 ** | F(3.62) = 0.90 | F(3.79) = 0.45 | F(3.4) = 8.23 ** |
OLS (Linear) | OLS (Logarithmic) | MGWR | |
---|---|---|---|
Inverse-distance (Euclidian) | |||
Moran’s index | 0.179 | 0.155 | −0.001 |
Z-score | 49.828 | 42.813 | −0.235 |
p-value | 0.001 | 0.001 | 0.814 |
Fixed-distance band | |||
Moran’s index | 0.107 | 0.091 | −0.002 |
Z-score | 63.892 | 54.611 | −0.916 |
p-value | 0.001 | 0.001 | 0.359 |
OLS | MGWR | ||
---|---|---|---|
Number of parameters | 7 | Effective number of parameters | 67.511 |
AIC | 514.253 | AIC | 254.153 |
Adjusted R2 | 0.611 | Adjusted R2 | 0.878 |
R2 | 0.622 | R2 | 0.909 |
Spatial kernel | Adaptive bisquare | ||
Criterion for optimal bandwidth | AICc | ||
Number of iterations used | 41 |
State-Led Systems | POP | AGRI_RENT | AGRI_LAND | URBAN_LAND | INCOME_CAP | G_GVA_IND |
---|---|---|---|---|---|---|
DK | 0.521 | −0.081 | 0.442 | 0.287 | 0.169 | 0.036 |
FI | 0.458 | −0.091 | 0.158 | 0.145 | 0.313 | 0.036 |
FR | 0.104 | 0.010 | 0.466 | 0.266 | 0.038 | 0.029 |
IE | −0.133 | 0.003 | 0.445 | 0.432 | −0.134 | 0.031 |
SE | 0.541 | −0.092 | 0.238 | 0.150 | 0.085 | 0.037 |
UK | −0.119 | 0.004 | 0.597 | 0.446 | −0.378 | 0.035 |
Market-led neo performative system | ||||||
AT | 0.039 | −0.081 | 0.391 | 0.487 | 0.352 | 0.031 |
CZ | 0.293 | −0.114 | 0.335 | 0.601 | 0.901 | 0.032 |
DE | 0.268 | −0.052 | 0.478 | 0.316 | 0.032 | 0.033 |
EE | 0.224 | 0.074 | 0.463 | 0.430 | 0.351 | 0.026 |
LT | 0.345 | 0.000 | 0.527 | 0.127 | −0.175 | 0.032 |
LV | −0.054 | 0.014 | 0.222 | 0.404 | −0.044 | 0.022 |
NL | 0.355 | −0.019 | 0.611 | 0.217 | −0.055 | 0.035 |
SI | 0.074 | −0.084 | 0.336 | 0.507 | 0.524 | 0.030 |
SK | 0.224 | −0.082 | 0.322 | 0.580 | 0.739 | 0.033 |
Conformative systems | ||||||
BE | 0.281 | 0.003 | 0.593 | 0.100 | −0.273 | 0.033 |
BG | 0.282 | 0.014 | −0.080 | 0.235 | −0.190 | 0.029 |
ES | 0.254 | 0.066 | 0.446 | 0.443 | 0.307 | 0.025 |
GR | 0.107 | 0.051 | 0.057 | 0.339 | −0.170 | 0.026 |
HR | 0.127 | −0.066 | 0.182 | 0.458 | 0.727 | 0.030 |
HU | 0.132 | −0.071 | 0.173 | 0.440 | 0.751 | 0.031 |
IT | −0.057 | −0.012 | 0.381 | 0.417 | 0.032 | 0.027 |
LU | 0.440 | −0.128 | 0.068 | 0.403 | 1.147 | 0.035 |
PT | 0.264 | 0.270 | 0.300 | 0.275 | 0.244 | 0.229 |
RO | 0.087 | 0.049 | 0.067 | 0.091 | 0.069 | 0.065 |
Misled performative system | ||||||
CY | 0.189 | 0.040 | −0.069 | 0.272 | −0.270 | 0.027 |
MT | 0.348 | −0.024 | 0.625 | 0.243 | −0.027 | 0.035 |
PL | 0.474 | −0.113 | 0.329 | 0.677 | 1.121 | 0.033 |
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Ustaoglu, E.; Williams, B. Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions. Land 2023, 12, 47. https://doi.org/10.3390/land12010047
Ustaoglu E, Williams B. Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions. Land. 2023; 12(1):47. https://doi.org/10.3390/land12010047
Chicago/Turabian StyleUstaoglu, Eda, and Brendan Williams. 2023. "Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions" Land 12, no. 1: 47. https://doi.org/10.3390/land12010047
APA StyleUstaoglu, E., & Williams, B. (2023). Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions. Land, 12(1), 47. https://doi.org/10.3390/land12010047