Co-designing climate-smart farming systems with local stakeholders: A methodological framework for achieving large-scale change

The literature is increasing on how to prioritize climate-smart options with stakeholders but relatively few examples exist on how to co-design climate-smart farming systems with them, in particular with smallholder farmers. This article presents a methodological framework to co-design climate-smart farming systems with local stakeholders (farmers, scientists, NGOs) so that large-scale change can be achieved. This framework is based on the lessons learned during a research project conducted in Honduras and Colombia from 2015 to 2017. Seven phases are suggested to engage a process of co-conception of climate-smart farming systems that might enable implementation at scale: (1) “exploration of the initial situation,” which identifies local stakeholders potentially interested in being involved in the process, existing farming systems, and specific constraints to the implementation of climate-smart agriculture (CSA); (2) “co-definition of an innovation platform,” which defines the structure and the rules of functioning for a platform favoring the involvement of local stakeholders in the process; (3) “shared diagnosis,” which defines the main challenges to be solved by the innovation platform; (4) “identification and ex ante assessment of new farming systems,” which assess the potential performances of solutions prioritized by the members of the innovation platform under CSA pillars; (5) “experimentation,” which tests the prioritized solutions on-farm; (6) “assessment of the co-design process of climate-smart farming systems,” which validates the ability of the process to reach its initial objectives, particularly in terms of new farming systems but also in terms of capacity building; and (7) “definition of strategies for scaling up/out,” which addresses the scaling of the co-design process. For each phase, specific tools or methodologies are used: focus groups, social network analysis, theory of change, life-cycle assessment, and on-farm experiments. Each phase is illustrated with results obtained in Colombia or Honduras

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Mine closure and its impact on the community : five years after mine closure in Romania, Russia and Ukraine

Against the backdrop of economic transition, several countries in Eastern Europe have undertaken far-reaching programs to restructure their coal sectors, which in the 1990s were in a state of deep crisis. One aspect of restructuring has been the closure of loss-making mines, which are often located in communities where the coal industry is the dominant employer, and the significant downsizing of the workforce. Mitigation efforts that are implemented at the time of mine closure (such as severance payments) are usually intended only for the laid-off workers. The authors examine the impact of mine closure on the entire community five years after mine closure in Romania, Russia, and Ukraine. Using quantitative and qualitative research methods and based on interviews with national, regional, and local experts, and members of the affected population, the authors describe the effect of mine closure and evaluate the various mitigation efforts that have been used by governments in such cases. They conclude with policy recommendations of broad relevance to programs of industrial restructuring in communities dominated by a single industry.Mining&Extractive Industry (Non-Energy),Municipal Financial Management,Environmental Economics&Policies,Banks&Banking Reform,Public Health Promotion,Municipal Financial Management,Health Monitoring&Evaluation,Mining&Extractive Industry (Non-Energy),Banks&Banking Reform,Environmental Economics&Policies

Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters

The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.

A Survey of the Structural Determinants of Local Emergency Planning Committee Compliance and Proactivity;Towards an Applied Theory of Precaution in Emergency Management

Millions of factories, chemical facilities, and highways store or convey extremely hazardous substances (EHS) in proximity to populated residential and commercial areas. The proliferation of hazardous chemicals in manufacturing has led to thousands of facilities that store and utilize them throughout the United States. There is inherent risk to neighborhoods and populated areas located near facilities that use and store hazardous chemicals. Local Emergency Planning Committees (LEPCs) were created in 1987 as stakeholder based, primarily volunteer organizations that address hazardous chemical accident mitigation. In addition, LEPCs were mandated with the intent of engaging communities in the debate about hazardous materials. Public safety has also increased in salience in the United States in particular since the terrorist attacks of September 11, 2001 and the 2005 Hurricane Katrina devastation in New Orleans. More recently, the earthquakes in Argentina, Chile, New Zealand, and most notably Japan have refocused efforts worldwide on examining policies and practices surrounding disaster management and response. This dissertation is an examination of compliance and proactivity in LEPCs and how use of limited resources influences these factors. A convenient sample of LEPCs in Ohio was surveyed to gather data for this causally probative study. LEPCs that are more compliant and proactive were expected to be in counties with larger, more urban populations that have more accident experience, and are expected to be in line with disaster management strategies that emphasize public involvement. The results of this study show a positive correlation between number of extremely hazardous substance facilities in a county and the compliance of that county\u27s LEPC. Other findings include limited emphasis on provision of information to the public. Emergency planning resources have been stretched further and further, with additional responsibilities of homeland security in addition to chemical safety tasks, and little to no additio

A Survey of the Structural Determinants of Local Emergency Planning Committee Compliance and Proactivity;Towards an Applied Theory of Precaution in Emergency Management

Millions of factories, chemical facilities, and highways store or convey extremely hazardous substances (EHS) in proximity to populated residential and commercial areas. The proliferation of hazardous chemicals in manufacturing has led to thousands of facilities that store and utilize them throughout the United States. There is inherent risk to neighborhoods and populated areas located near facilities that use and store hazardous chemicals. Local Emergency Planning Committees (LEPCs) were created in 1987 as stakeholder based, primarily volunteer organizations that address hazardous chemical accident mitigation. In addition, LEPCs were mandated with the intent of engaging communities in the debate about hazardous materials. Public safety has also increased in salience in the United States in particular since the terrorist attacks of September 11, 2001 and the 2005 Hurricane Katrina devastation in New Orleans. More recently, the earthquakes in Argentina, Chile, New Zealand, and most notably Japan have refocused efforts worldwide on examining policies and practices surrounding disaster management and response. This dissertation is an examination of compliance and proactivity in LEPCs and how use of limited resources influences these factors. A convenient sample of LEPCs in Ohio was surveyed to gather data for this causally probative study. LEPCs that are more compliant and proactive were expected to be in counties with larger, more urban populations that have more accident experience, and are expected to be in line with disaster management strategies that emphasize public involvement. The results of this study show a positive correlation between number of extremely hazardous substance facilities in a county and the compliance of that county\u27s LEPC. Other findings include limited emphasis on provision of information to the public. Emergency planning resources have been stretched further and further, with additional responsibilities of homeland security in addition to chemical safety tasks, and little to no additio