The Effects of Environmental Regulation on Technology Diffusion: The Case of Chlorine Manufacturing

We use a hazard model to estimate the effect of environmental regulation on the diffusion of membrane cell production technology in the chlorine manufacturing industry. We estimate the effect of regulation on both the adoption of the membrane technology at existing plants and on the exit of existing plants using older technologies. We find that environmental regulation did affect the diffusion of the cleaner technology in the chlorine industry. However, it did so not by encouraging the adoption of membrane cells by existing facilities, but by reducing the demand for chlorine and hence encouraging the shutdown of facilities using the environmentally inferior options.Regulation, Technological change, Environment, Hazard model

Essays on Technology Adoption in Senegal

African agriculture is characterized by very low average productivity. This results in a very high yield gap, i.e. the average yields achieved by farms are up to 90% below the yields that can be achieved by applying proven best-practice technologies. A central problem of low agricultural productivity is, therefore, technology adoption, i.e. the question of why farms do not apply available best-practice technologies. In this context, this dissertation investigates the mechanisms of technology adoption using a unique farm data set of more than 4000 farms in Senegal and innovative econometric methods. A first descriptive analysis reveals dual Senegalese agriculture with a small percentage of farms using modern technologies, i.e. irrigation, use of mineral fertilizers and pesticides and improved seeds, and a majority of farms using traditional extensive farming without the use of purchased inputs and irrigation. For example, the use of N-fertilizer in the majority of traditional farms is less than 30kg/ha while modern farms use more than 300kg/ha. While a shift from traditional to modern agriculture at the macro level has a clear positive effect on food security and rural development, the question arises as to the key micro-level barriers that prevent traditional farms from using modern technologies. While the potential obstacles have been identified from the theoretical literature, i.e. transaction costs in credit, labor, goods, and insurance markets as well as imperfect technological knowledge of farmers, for practical agricultural policy the question arises as to which are the central causes in a specific empirical case. This is particularly important because the efficient agricultural policy measures to reduce these obstacles differ significantly depending on the specific obstacle. In this interesting and highly relevant area of agricultural policy, the present study makes central contributions by applying innovative econometric methods for the microeconomic analysis of technology adaptation, i.e. the concrete obstacles to the application of modern agricultural technology at the farm level. In total, the dissertation comprises 4 contributions. In the first contribution, a flexible bivariate probit model is applied to analyze the joint use of certified seed and mineral fertilizer in rice and peanut production. While the flexible versus the standard probit model is theoretically and statistically preferable, both approaches lead to the same key policy implications. The second paper analyzes the impact of multiple technology decisions on technical efficiency and yield using rice production as an example. On a methodological level, the paper combines a metafrontier approach with a multinominal treatment-effects model to take into account the heterogeneity in rice production and potential selection bias in the choice of technologies. A remarkable result of the analyses is the identification of significant knowledge gaps as a central obstacle for the use of modern inputs. The third paper examines the importance of yield risk for the use of modern inputs and its significance for the income and food security of agricultural households. Methodologically, an endogenous switching regression model is used to adequately analyze the treatment effects of modern input use. In the fourth paper, an interdependent farm household model is used as a theoretical approach to analyzing participation in relevant agricultural input and output markets. Transaction costs are a central determinant of the market participation of agricultural enterprises. Since transaction costs can be specific for different input and output markets, different market regimes result, including complete self-sufficiency, selective participation in specific output or input markets, and complete market participation. Methodologically, a multinomial endogenous treatment effects model is applied to empirically analyze the market participation decisions of individual farm households. Interestingly, farms participate selectively in output and input markets. This implies market-specific transaction costs, which cannot be explained by general factors such as infrastructure and market distance, but rather, for example, by specific social network structures that determine selective access to markets

The Survival of the Conformist: Social Pressure and Renewable Resource Management

This paper examines the role of pro-social behavior as a mechanism for the establishment and maintenance of cooperation in resource use under variable social and environmental conditions. By coupling resource stock dynamics with social dynamics concerning compliance to a social norm prescribing non-excessive resource extraction in a common pool resource (CPR), we show that when reputational considerations matter and a sufficient level of social stigma affects the violators of a norm, sustainable outcomes are achieved. We find large parameter regions where norm-observing and norm-violating types coexist, and analyze to what extent such coexistence depends on the environment.Cooperation, Social Norm, Ostracism, Common Pool Resource, Evolutionary Game Theory, Replicator Equation, Agent-based Simulation, Coupled Socio-resource Dynamics

The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling

textThis thesis presents a step-by-step implementation of the Solar Energy Consumer Agent Decision (SECAD) model: an empirically-grounded multi-agent model of residential solar photovoltaic (PV) adoption with an integrated geospatial topology. Solar PV diffusion is a complex system with geographic heterogeneity, uncertain information, high financial risk, and important social interaction and feedback effects between consumers. A key limitation for agentbased models in human socio-technical systems is the integration of empirical patterns in the model structure, initialization, and validation efforts. This limitation is addressed though highly granular and interlocking data-streams from the geographic, social network, financial, demographic, and decision-making process of real households in the study. The fitted and validation model is used to simulate implementation of potential policies to inform decision-makers: i) Targeted informational dissemination campaigns, ii) Tiered rebates, iii) Locational pricing, and iv) Alternative rebate schedules. Informational campaigns can increase cumulative installations by as much as 12%, but vary greatly in their effectiveness based on which agents are targeted. Simulations suggest that by lowering the cost barrier to lower wealth households through a slightly higher rebate (+$0.25/Watt), the mean difference in wealth between solar adopters and non adopters could be reduced by 22.6$0.25 higher offering increased the percentage of adopters in the target area from less than 1% to over 10%. Relative to flatter rebate schedules, sharply decreasing schedules are effective in terms of motivating adoption but inefficient in small markets. It is our hope that this work will provide a working example for other agent-based models of human socio-technical systems as well as provide insight into the likely outcomes of novel policy-levers such as those described above.Energy and Earth ResourcesPublic Affair

Future capacity growth of energy technologies: are scenarios consistent with historical evidence?

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation

2008,06: Evolutionary modelling in economics : a survey of methods and building blocks

In this paper we present an overview of methods and components of formal economic models employing evolutionary approaches. This compromises two levels: (1) techniques of evolutionary modelling, including multi-agent modelling, evolutionary algorithms and evolutionary game theory; (2) building blocks or components of formal models classified into core processes and features of evolutionary systems - diversity, innovation and selection - and additional elements, such as bounded rationality, diffusion, path dependency and lock-in, co-evolutionary dynamics, multilevel and group selection, and evolutionary growth. We focus our attention on the characteristics of models and techniques and their underlying assumptions. -- bounded rationality ; evolutionary algorithms ; evolutionary game theory ; evolutionary growth ; innovation ; multilevel evolution ; neo-Schumpeterian models

Agroforestry Adoption in Ethiopia: Innovation Systems and Farm Level Analysis

Agroforestry (AF) or agroforestry innovation (AFI) production has long been and continues to be a component of the mixed farming system of Ethiopian and smallholder farmers worldwide. Interventions continue introducing new or improved management practices, species, and techniques to raise AFI's livelihood and natural resource management contributions. Despite considerable efforts, the adoption of these AFI continues to be limited, as proved by several adoption studies and development efforts. Formal and informal studies were conducted for decades to understand the problems for the low adoption of various AFI. Nevertheless, these studies generated redundant and marginally growing important information as it has weakly altered the course of development approaches and policy regulations. Learning from previous studies, researchers have been requesting more robust studies that help address existing knowledge gaps on adopting AFI. To respond to these calls, this PhD project examined the factors affecting the adoption of AFI by smallholders and Ethiopian farmers as a case study. The project builds upon previous studies to explore the diverse perspectives that influence the adoption of AFI. Literature assessment of recent studies indicated that several factors belonging to farmers and institutions influence the adoption of AFI. Simultaneously, we discovered that some issues were explored frequently (e.g., socioeconomic factors), whereas others (e.g., psychological factors) were largely ignored. Besides, researchers have followed the static assumption (i.e., adopt or non-adopt) and failed to learn the adoption process beyond a one-time decision. Additionally, the studies focused on discrete factors and activities and failed to comprehend the diverse perspectives and factors and their combined effect on eventual AFI adoption. Ultimately, learning from the larger adoption science and previous studies, we developed a comprehensive framework, 'AFI adoption framework' (chapter 4.1), that supports the meaningful assessment of adoption practices and comprehensively discovers factors influencing AFI adoption. The framework encompassed three compartmentalized and yet interlinked components that influence AFI adoption under smallholder contexts. The framework commended both distinct studies for exhaustive elaboration and simultaneously suggested holistic examination. Besides, it recommended minor and major modifications to the research approaches, such as proper treatment of variables in econometric models, incorporation of variables related to the psychological status, and employment of robust tools such as the real-options approach for profitability analysis. Based on this framework, we designed a project and conducted fieldwork in the Amhara region of Ethiopia, a typical smallholder context. We explored the household contexts (i.e., farm level and psychological), system level features, and innovation characteristics influencing smallholders' AFI adoption decisions. It employed mixed conventional and advanced analytical tools comprising content analysis, econometric models, principal component analysis, and financial discounting methods. Advanced methods comprehend process analysis and adoption dynamism. The results from discrete analysis indicated that socioeconomic factors, psychological constructs, system level features, and innovation attributes influence AFI adoption. Regarding innovation characteristics, the different attributes are foundations for undertaking AFI adoption decisions of smallholder farmers. Beyond adopt-non-adopt concepts, we found farmers continuously undertake follow-up adoptions of varying extents such as reduced, maintained, and increased. Based on our query and comparable to existing frameworks, the newly developed 'AFI adoption framework' is more reasonable to meaningfully investigate factors influencing AFI (and agricultural innovations) adoption under smallholder contexts. However, there is a need for precaution while employing the framework to more clearly discern the adoption process and reflect the integration among the factors and activities involved from the development to the adoption of AFI. This dissertation excluded empirical analysis of profitability and holistic assessment due to the voluminous nature of the dissertation.:PREFACE ii ACKNOWLEDGEMENTS iii SUMMARY iv ZUSAMMENFASSUNG vi LIST OF FIGURES ix LIST OF TABLES xi ACRONYMS xi 1. INTRODUCTION 1 1.1. Agroforestry in Ethiopia 1 1.2. Problem statement 4 1.3. Objectives and research questions 6 1.4. Scope of the study 7 1.5. Dissertation structure 8 2. CONCEPTUAL FRAMEWORK 11 2.1. The adoption concept 11 2.2. Theoretical frameworks on adoption 12 2.3. The critique and research context 16 2.4. The AFI adoption analytical framework 17 2.5. Description of links between objectives and research questions 19 3. RESEARCH METHODOLOGY 21 3.1. Description of the study area 21 3.2. Selection of innovations and farmers 22 3.3. Research methods 23 3.3.1. Data collection methods 23 3.3.2. Sampling technique and sample size 24 3.3.3. Data analysis 25 4. RESULTS 26 4.1. Agroforestry adoption as a systems concept: a review 27 4.2. Can a sequential analysis provide a more robust understanding of farmers’ adoption decisions? an example from an agroforestry adoption study in Ethiopia 58 4.3. Farmers’ intentions towards sustained agroforestry adoption: an application of the theory of planned behavior 88 4.4. Adoption under the influence of innovation attributes: the case of agroforestry innovations from Ethiopia 111 4.5. Influence of system level factors on adoption of agroforestry innovations 141 5. SYNTHESIS and CONCLUSION 170 5.1. Synthesis of key findings 170 5.1.1. State of AFI adoption research in SSA 170 5.1.2. Persistent calls for rigorous research 172 5.1.3. Critical factors affecting AFI adoption 173 5.1.4. Conceptualizing adoption as a complex decision process 175 5.2. Reflections on research method, theoretical framework, and generalization 177 5.2.1. Reflection on research methods and analytical generalization 177 5.2.2. Reflection on the theoretical framework and theoretical contribution 179 5.3. Outlook and suggestions 184 5.4.1. Recommendations for future research 185 5.4.2. Development and policy recommendations 186 5.5. Limitations of the study 186 REFERENCES 187 APPENDICES 19

AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model

This paper presents a dynamic agent-based model of land use and agricultural production under environmental boundaries, finite available resources and endogenous technical change. In particular, we model a spatially explicit smallholder farming system populated by boundedly-rational agents competing and innovating to fulfill an exogenous demand for food, while coping with a changing environment shaped by their production choices. Given the strong technological and environmental uncertainty, agents learn and adaptively employ heuristics which guide their decisions on engaging in innovation and imitation activities, hiring workers, acquiring new farms, deforesting virgin areas and abandoning unproductive lands. Such activities in turn impact farm productivity, food production, food prices and land use. We firstly show that the model can replicate key stylized facts of the agricultural sector. We then extensively explore its properties across several scenarios featuring different institutional and behavioral settings. Finally, we simulate the model across different applications considering deforestation and land abandonment; human-induced soil degradation; and climate impacts. AgriLOVE offers a flexible simulation environment to study the endogenous emergence of different agricultural production regimes from the interaction of spatially dispersed farms subject to resource constraints, spatial influence and climate change

Technology Adoption, Impact, and Extension in Developing Countries’ Agriculture: A Review of the Recent Literature

Given the stagnant agricultural productivity and persistent food insecurity in low-income countries—notably in sub-Saharan Africa (SSA)—there has been continued interest in the adoption of new technology and its impact on productivity in these regions. Interestingly, there are signs of Green Revolution in maize and rice in SSA, reflected in sharply increasing yield trends in advanced regions. To increase crop yields and sustain yield gains, recent case studies of technology adoption unanimously recommend the adoption of integrated farm management systems, particularly in SSA. On the other hand, since the 2010s, there have been increasing numbers of studies on social network or farmer-to-farmer technology extension. These studies explore more efficient extension systems than traditional public-sector extension approaches. This article reviews both recent case studies of technology adoption and its productivity impacts as well as studies on agricultural extension to identify common findings, shortcomings, and major remaining issues

Evolutionary macroeconomic assessment of employment and innovation impacts of climate policy packages

Climate policy has been mainly studied with economic models that assume representative, rational agents. Such policy aims, though, at changing carbon-intensive consumption and production patterns driven by bounded rationality and other-regarding preferences, such as status and imitation. To examine climate policy under such alternative behavioral assumptions, we develop a model tool by adapting an existing general-purpose macroeconomic multi-agent model. The resulting tool allows testing various climate policies in terms of combined climate and economic performance. The model is particularly suitable to address the distributional impacts of climate policies, not only because populations of many agents are included, but also as these are composed of different classes of households. The approach accounts for two types of innovations, which improve either the carbon or labor intensity of production. We simulate policy scenarios with distinct combinations of carbon taxation, a reduction of labor taxes, subsidies for green innovation, a price subsidy to consumers for less carbon-intensive products, and green government procurement. The results show pronounced differences with those obtained by rational-agent model studies. It turns out that a supply-oriented subsidy for green innovation, funded by the revenues of a carbon tax, results in a significant reduction of carbon emissions without causing negative effects on em ployment. On the contrary, demand-oriented subsidies for adopting greener technologies, funded in the same manner, result in either none or considerably less re- duction of carbon emissions and may even lead to higher unemployment. Our study also contributes insight on a potential double dividend of shifting taxes from labor to carbon