Risk Management of Marine Capture Fisheries under Climate Change: Taking into Consideration the Effects of Uncertainty

Multiple changes in marine resources (e.g., abundance, movements, distribution, biomass) caused by climate change are critical operational risks, leading to production uncertainty for capture fisheries. Therefore, risk management measures of coastal and offshore fisheries are critical issues in terms of operational sustainability. In this study, a questionnaire survey data set collected from fishers was analyzed using descriptive statistics, factor analysis, and a structural equation model (SEM) to examine fishers’ perceptions and the relationships among risk sources, production uncertainty, and adaptation measures. The results revealed that significant negative impacts existed between risk sources and adaptation measures, which means risk sources cannot directly influence risk management measure selection. However, production uncertainty could be an important mediator for risk management, thus most respondents think that mitigating production uncertainty is necessary. Eventually, the results could provide managerial implications for the fishery operators, policymakers and the government agencies.

Smallholding farmers’ resilience towards economic and ecological disruption of oil palm plantations

Indonesian smallholder oil palm plantations are facing both economic and ecological challenges, therefore the farmers struggle to be resilient. This study constructs two purposes, (1) to measure the resilience level of smallholder plantations, and (2) to assess the effect of economic and ecological disruption on smallholders’ resilience. We interviewed a sample of 120 smallholders in South Bengkulu regency, Bengkulu Province, Indonesia. The methodology deploys a quantitative method (statistics and econometrics) to analyze the effect of disruptive incidents on smallholders’ resilience. Resilience is indicated by farmers’ ability to adapt to changes, to recover from downturn business conditions or catastrophes, to anticipate risk, and to innovate new designs of farming activities. Resilience is categorized as less or more resilient (binary). The economic disruption is triggered by production, market, and investment circumstances. Meanwhile, ecological disruption is resulted from natural disasters, climate change, farmer’s treatment of the land, land fire, and government environmental policy. The result shows that more than 60% of smallholder oil palm plantations in Bengkulu Province are less resilient. Production uncertainty, bargaining position, climate change, and environmentally unfriendly farming behaviours increase the possibility of lowering smallholders’ resilience level.

Instability of Textile Production in Pakistan: Stochastic Frontier Model Approach

Textile industry is the largest industry of Pakistan and like other industries it is facing not only high and escalating cost of electricity and gas but also lack of market access. This study has computed production uncertainty (PU) due to technical inefficiency (TIE) of textile exporting and manufacturing (TEM) firms in Pakistan. We has obtained data from annual reports of 98 companies for the year 2017-18. We has applied stochastic production frontier approach with half normal distribution of ui. PU with confidence bounds had been computed. Inefficiencies (ui/εi) were statistically significant at 5 % level of significance. The mean PU was 0.0045. The computed scores of PU of TEM firms in Pakistan during 2017-18 showed that maximum numbers of firms had their PU score low and close to minimum PU score and very few firms had high PU score and close to maximum PU score. Keywords: Textile Manufacturing Firms, Production Uncertainty, Technical Inefficiency, Confidence Bounds, MLE Technique, Cobb-Douglas Production Function.

Forecast trajectories for the production of a renewable virtual power plant able to provide ancillary services

<p>The aggregation of multiple renewable plants located in distinct climate zones, using different energy sources, enables to reduce the production uncertainty when compared to the production of a single plant. Such aggregations, controlled by a Virtual Power Plant (VPP) system, are good candidates for the provision of ancillary services. Stochastic optimization models are available to optimize biddings on ancillary services and energy markets (see for instance [1]). These models require trajectories of the renewable VPP production that anticipate production uncertainty and reproduce correctly the temporal correlations observed in the production signal. This is particularly important in ancillary services markets, where a reserve bid must be guaranteed over a production duration or validity period during which power fluctuations are significant (e.g. lasting currently 24 hours on the European common market for Frequency Containment Reserve, with a foreseen evolution to 4 hours by July 2020 [2]). <br>Production trajectories may be obtained by coupling probabilistic forecasts and a model of temporal dependencies between forecast horizons [3] and possibly spatial dependencies in the case of a multivariate forecast at the scale of a region or a portfolio [4]. In the case of a renewable VPP, the aggregated production is primarily of interest. In this work, we propose a methodology to generate trajectories of aggregated production from probabilistic forecasts obtained with decision-tree based models or neural networks. A copula models the dependency between forecast horizons and the space defined by the plants contained in the aggregation. The model is tested in a day-ahead forecasting configuration on a 54 MW VPP comprising 15 plants with 3 different energy sources (Photovoltaics, Wind, Hydro). The comparison of trajectories generated from a direct forecast of the aggregated production and from forecasts at lower levels of the aggregation shows that the latter solution reproduces with more accuracy the temporal variability of the aggregated production over the whole horizon range, especially when Photovoltaics dominates the production capacities in the aggregation (15 % improvement of the Variogram Score).<br> [1]: Soares, T., & Pinson, P. (2017). Renewable energy sources offering flexibility through electricity markets. Technical University of Denmark.<br>[2]: ENTSO-E. (2018). TSO’s proposal for the establishment of common and harmonised rules and processes for the exchange and procurement of Balancing Capacity for Frequency Containment Reserves (FCR) TSOs’ proposal for the establishment of common and harmonised rules and pro-c, (October), 1–9.<br>[3]: Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009). From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy, 12(1), 51–62. <br>[4]: Golestaneh, F., Gooi, H. B., & Pinson, P. (2016). Generation and evaluation of space–time trajectories of photovoltaic power. Applied Energy, 176, 80–91. </p>