Quantifying relationships between governance, agriculture, and nature: empirical-statistical- and pattern-oriented modeling Quantifying relationships between governance, agriculture, and nature Menno Mandemaker Menno Mandemaker INVITATION to the public defense of my thesis: Quantifying relationships between governance, agriculture, and nature Empirical-statistical- and pattern-oriented modeling Monday 1 December, 2014 at 1:30 p.m. in the Aula of Wageningen University Generaal Foulkesweg 1a Wageningen Menno Mandemaker Paranimfs: Marcel Kuperus [email protected] Susan Boonman-Berson [email protected] Quantifying relationships between governance, agriculture, and nature: empirical-statistical- and pattern-oriented modeling Menno Mandemaker Thesis committee Promotor Prof. Dr A. Veldkamp Dean of the Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands Co-promotors Dr M.M. Bakker Associate professor, Land Use Planning Group Wageningen University Dr J.J. Stoorvogel Associate professor, Soil Geography and Landscape Group Wageningen University Other members Prof. Dr A.K. Bregt, Wageningen University Dr P. Reidsma, Wageningen University Dr D.T. Robinson, University of Waterloo, Canada Dr E. Hatna, Johns Hopkins University, Baltimore, United States This research was conducted under the auspices of the C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC). Quantifying relationships between governance, agriculture, and nature: empirical-statistical- and pattern-oriented modeling Menno Mandemaker Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. Dr M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Monday 1 December 2014 at 1:30 p.m. in the Aula. Menno Mandemaker Quantifying relationships between governance, agriculture, and nature: empirical-statistical- and pattern-oriented modeling, 178 pages. PhD thesis, Wageningen University, Wageningen, NL (2014) With references, with summaries in Dutch and English ISBN 978-94-6257-144-0 Table of contents Chapter 1. General introduction 11 1.1. Ways of including governance into land-use modeling 12 1.1.1. Scientific and societal relevance 12 1.1.2. Background 13 220.127.116.11. Governance 13 18.104.22.168. Empirical-statistical- and process-based modeling 15 22.214.171.124.1. Empirical-statistical modeling 16 126.96.36.199.2. Process-based modeling 18 1.2. Research questions 19 1.2.1. Empirical-statistical modeling 19 188.8.131.52. Research question 1 19 184.108.40.206. Research question 2 19 220.127.116.11. Research question 3 19 1.2.2. Process-based modeling 19 18.104.22.168. Research question 4 19 1.3. Structure guide 20 Chapter 2. The role of governance in agricultural expansion and 23 intensification: A global study of arable agriculture 2.1. Introduction 25 2.2. Data and methods 27 2.2.1. Data 28 22.214.171.124. Production indicators 28 126.96.36.199. Governance indicators 30 188.8.131.52. Control indicators 32 2.2.2. Methods 32 184.108.40.206. Between-groups analysis 32 220.127.116.11. Within-groups analysis 33 2.3. Results 34 2.3.1. Between-groups analysis 34 2.3.2. Within-groups analysis 38 2.4. Discussion 39 2.5. Conclusions 41 Chapter 3. Quantifying effects of governance on nature dynamics in 43 Europe: A cross-national comparison for 1990–2006 3.1. Introduction 45 3.2. Data and methods 47 3.2.1. Data 47 18.104.22.168. Indicator of overall quality of governance 47 22.214.171.124. CORINE Land Cover (CLC) 50 3.2.2. Methods 51 126.96.36.199. Change in average patch size and total area of nature 51 188.8.131.52. Relationships with overall quality of governance 53 3.3. Results 54 3.3.1. Characterization of dominant spatiotemporal processes of 54 fragmentation and expansion of nature 3.3.2. Relationships with overall quality of governance 56 3.4. Discussion 58 3.4.1. Characterization of dominant spatiotemporal processes of 58 fragmentation and expansion of nature 3.4.2. Relationships with overall quality of governance 59 3.4.3. Limitations of this study and suggestions for further research 60 3.5. Conclusions 61 Appendix 62 Chapter 4. A pattern-oriented individual-based land-use-transition 65 model: utility maximization at varying levels of complexity and rationality (CORA) 4.1. Nomenclature 67 4.2. Introduction 67 4.3. Model 68 4.3.1. Land-use states 68 4.3.2. Land-use-state transitions 69 4.3.3. Structure 69 4.3.4. Aggregate uncertainty 70 4.3.5. Model output 71 4.4. Temporal and spatial mechanisms 71 4.4.1. Temporal mechanisms 71 184.108.40.206. Utility maximization 71 220.127.116.11. Anticipation 74 18.104.22.168. Utility feedbacks and regrowth of nature 80 4.4.2. Spatial mechanisms 85 22.214.171.124. Co-operation 85 4.5. Discussion 89 4.5.1. Emergence and self-organization 89 4.5.2. Temporal mechanisms 90 4.5.3. Spatial mechanisms 91 4.5.4. Intensification 91 4.5.5. Validation and value added 92 4.6. Conclusions 93 Chapter 5. Pattern-oriented individual-based modeling of spatially 95 explicit agricultural landscapes: A scenario analysis of effects of state-centric governance and top-down policy instruments on protection of nature 5.1. Introduction 97 5.2. Methods 98 5.2.1. CORA model 98 126.96.36.199. Land-use states 98 188.8.131.52. Land-use-state transitions 98 184.108.40.206. Structure 99 220.127.116.11. Model output 100 5.2.2. Spatial distribution of utilities and costs 100 5.2.3. State-centric governance settings and top-down policy scenarios 101 18.104.22.168. State-centric governance settings 101 22.214.171.124. Top-down policy scenarios 102 5.3. Results 104 5.3.1. System-state variables 104 126.96.36.199. Land use 104 188.8.131.52. Utility 105 5.3.2. Land-use patterns 107 5.4. Discussion 111 5.4.1. Within state-centric governance settings 111 184.108.40.206. Low Entrepreneurial Awareness and 111 Low Quality of Investment Climate (I) 220.127.116.11. Low Entrepreneurial Awareness and 112 High Quality of Investment Climate (II) 18.104.22.168. High Entrepreneurial Awareness and 112 Low Quality of Investment Climate (III) 22.214.171.124. High Entrepreneurial Awareness and 113 High Quality of Investment Climate (IV) 5.4.2. Between state-centric governance settings 113 126.96.36.199. Baselines 113 188.8.131.52. Top-down subsidy scenarios 115 184.108.40.206. Top-down fine scenarios 116 5.5. Conclusions 117 Chapter 6. Synthesis 119 6.1. Ways of including governance into land-use modeling 121 6.1.1. Empirical-statistical modeling 121 220.127.116.11. Research question 1 123 18.104.22.168. Research question 2 124 22.214.171.124. Research question 3 126 6.1.2. Process-based modeling 129 126.96.36.199. Research question 4 130 6.1.3. Comparing empirical-statistical- and process-based modeling 131 6.2. Key conclusions 133 6.2.1. Recommendations for further research 134 References 137 Summary 149 Samenvatting 155 Acknowledgements 163 About the Author 167 List of Publications 171 Education Certificate 175 1 General introducti on R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 12 Chapter 1 1.1. Ways of including governance into land-use modeling 1.1.1. Scientific and societal relevance Land use is characterized by arrangements, activities, and inputs by humans to produce, change, or maintain particular land-cover types (Di Gregorio and Jansen, 1998), establishing a direct interface between human behavior and observed land cover. Consequently, land use is generally the outcome of a complex process of interfacing biophysical, geographical, and socio-economic determinants (Kok and Veldkamp, 2001). It has therefore been recognized that multi-disciplinary research is crucial for the progress in land-use science (Veldkamp and Verburg, 2004), and that integration of social- and natural-science approaches is required to achieve improved understanding of how land use impacts on the ecosystems in which it is embedded (Veldkamp and Lambin, 2001; Brown et al., 2013). Owing to their quantitative nature, economic models (general- and partial equilibrium models in particular) have long since been integrated with land-use models (Irwin and Geoghegan, 2001), together with geographic information systems. However, a very important and extremely challenging component that is still largely missing from such integrated land-use models is how to model “Governance by governing” behavior (188.8.131.52. Governance, Table 1) that occurs not only for economic reasons but also for other governance-related reasons (Irwin and Geoghegan, 2001). An improved understanding of how complex processes of both socio-political and “Economic governance” (184.108.40.206. Governance, Table 1) shape land-use patterns would represent a crucial step forward. It could allow, for example, for land-use policies that are better suited to the high complexity common to governing behavior encountered across various levels of geographical spatial scale. Furthermore, given the increasing pressures of rising global food demand and agricultural land use on nature (Lambin et al., 2000; Verburg et al., 2013), an improved understanding of this complexity is desirable, so that further negative effects of these increasing pressures can be minimized. However, this requires further disciplinary integration of—and therefore reconciliation of fundamental differences between—social and natural science perspectives of governance and land use, respectively. In the social sciences, predominantly social-constructivist perspectives of governance have been adopted for meaningful interpretations of observed governing behavior (220.127.116.11. Governance, Table 1), consistent with viewing society as an abstract whole in which individuals create governance networks- and knowledge through social interactions. R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 13 General introduction 1 1.1.2. Background 18.104.22.168. Governance Despite the clear added value of qualitative perspectives of governance (Table 1), such perspectives are only little concrete and do not provide any handholds for quantitative analysis. That is, the adoption of a more social-positivistic perspective of governance is unavoidable if we wish to integrate social-science based concepts of governance with quantitative natural-science based concepts of land use. Although hardly any social-positivistic concepts of governance have been developed due to its qualitative nature, the field of adaptive governance (Table 1, “Adaptive governance”) does have the ambitious goal of developing new concepts of governance that can handle the inherent complexity and unpredictability of dynamic social-ecological systems through self-organization and adaptation. Furthermore, the more modern descriptions of governance in Table 1 have been developed largely as a critique of state-centric governance, also referred to as hierarchical governance (Hill and Lynn, 2004), the government perspective (Rhodes, 1997), command and control systems of governance (Kooiman, 1993), or the classical-modernist approach of governance (Hajer and Wagenaar, 2003). Hitherto, however, the traditional provision of information to policymakers by science remains the foremost factor at the basis of real-world governance. Furthermore, as the research in this thesis is concerned with real-world effects of governance on land use it would not benefit from an attempt at integrating quantitative process-based relationships into social-constructivist perspectives of governance. Although land use is also studied from more qualitative perspectives, e.g., in the fields of land-use policy- or planning, or landscape architecture, quantitative studies usually do have a greater scientific impact. This derives from the importance of future projections and predictions of land use to integrated impact assessments regarding e.g., climate change, soil degradation, or loss of biodiversity. That is, these projections and predictions may only be obtained through quantitative studies (e.g., simulation models, scenario analyses, or empirical-statistical analyses). Moreover, real- world governance of land results from policy designs that are directly or indirectly based on empirical numeric data (e.g., from such integrated impact assessments) that provide quantitative and verifiable evidence for concrete relationships, or critical conditions or thresholds relevant to specific phenomena. R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 14 Chapter 1 Therefore, objective and quantitative indicators of governance should be integrated into quantitative empirical-statistical- and process-based modeling approaches that attempt to explain how land-use patterns come to exist. This way, it might be possible to assess impacts of complex decision-making processes on land-use patterns using numeric data of quantified social-science concepts (both at an aggregate level and at the individual level), which might in turn allow for better real-world governance of such impacts across various levels of geographical spatial scale (Cash et al., 2006). Table 1. Different qualitative perspectives of governance. State-centric governance State-centric governance refers to the traditional viewpoint in which the state is the center of political power and authority (Rhodes, 1997; Pierre, 2000; Kooiman, 2003). The state exerts control over society, economy, and resources by setting the agenda of societal problems, deciding upon which policy goals and means to follow, and by top-down implementation of policies. Good governance The earliest noteworthy form of ‘modern’ governance was in the field of economic development, where the World Bank and other international organizations emphasized the need for ‘sound’ or ‘good governance’. Stressing the political, administrative, and economic values of legitimacy and efficiency (Rhodes, 2000; Kaufmann et al., 2009), this entails reducing public spending; investing in health, education, and social security; promoting private sectors by regulatory reform; reinforcing private banking; tax reforms; and greater transparency and accountability in government-corporate relationships (Kiely, 1998; Philip, 1999; Rosenbaum and Shepherd, 2000; Woods, 2000). Economic governance Economic governance is concerned with markets and their governing institutions. In this approach, the fields of new institutional economics (Williamson, 1975, 1985, 1996), economic sociology (Smelser and Swedberg, 1994), and comparative political economy (Hollingsworth and Boyer, 1997; Crouch and Streeck, 1997; Hall, 1999) have been brought together by emphasizing that markets are not spontaneous social orders, but have to be created and maintained by institutions. These provide, monitor, and enforce rules which among other things regulate property rights, contracts, competition, and reduce risk, asymmetry of information, and uncertainty. Governing without government The possibility of governing without government stems from international relations theory (Rosenau and Czempiel, 1992). Traditionally, international relations are seen to be characterized by competing and interdependent states that acknowledge no other authority than their own (Lieshout, 1995). However, international politics are decreasingly seen as solely being a cooperation between independent states. Rather, they are increasingly seen as new forms of international governance that sustain mechanisms designed to ensure coherence, stability, and safety (Rosenau, 2000). These mechanisms, traditionally found in governments, are increasingly found in international organizations, treaties, and regimes.