Practical Alternatives to Estimate Opportunity Costs of Forest Conservation

Numerous studies have shown the merits of targeting the costs of conservation besides environmental benefits and aligning payments for ecosystem services with incurred costs. However, cost-effective and precise estimation of site specific opportunity costs is a major challenge. In this paper we test two approaches to estimate opportunity costs of conservation: One approach derives opportunity costs from annual land rents, and the other models regresses opportunity costs on easily obtainable and difficult to manipulate spatial and socio-economic independent variables such as soil quality. None of these approaches appeared to estimate opportunity costs sufficiently well. But since this judgment is based on how well the estimates compare to the reference opportunity costs, which were computed from farm budgets, we also considered potential flaws in the reference data and tested their plausibility. The tests confirmed the plausibility of data. Based on the results presented in this paper none of the two cost estimation approaches can be recommended for practical application in conservation programs. Yet, further research is necessary to confirm these findings giving special attention to the techniques that are applied to deliver reference point data on opportunity costs.Resource /Energy Economics and Policy,

Opportunity Costs as a Determinant of Participation in Payments for Ecosystem Service Schemes

Landholders are generally assumed to be willing to participate in payments for ecosystem service (PES) schemes if the offered payment exceeds the opportunity cost of participation. The calculation of opportunity costs is often based on historic financial data such as net returns of the formerly practiced land use. Reliable estimates of opportunity costs are required especially in flexible, cost-aligned payment schemes with differentiated payments at the farm scale. We question whether opportunity cost estimates that do not consider personal landholder characteristics such as risk considerations, information access and non-monetary personal preferences (e.g. for traditional land use practices) are sufficient to explain a landholder's decision to enrol land in PES. To test these assumptions, a PES adoption model was developed for hypothetical adoption decisions by 178 landholders in Costa Rica. The model explained up to 73.5% (Nagelkerkes pseudo R2) of adoption variance. The results confirm that adoption is not determined by financial costs alone. Trust in state institutions, for example, was highly significant. The results call for more integrated methods of opportunity cost estimation such as inverse auctions. Their strength lies, among others, in that all adoption determinants are potentially expressed in the landholder's bid.Resource /Energy Economics and Policy,

Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System

One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approachComment: 8 pages, 4 figures. To be published in 2023 IEEE Conference on Control Technology and Applications (CCTA) proceeding

Proposing A Supply Chain Analytics Reference Model As Performance Enabler

Nowadays firms have to react quickly to changing markets creating a need for accurate forecasts of demand and supply. In a data-rich environment as it is within the field of supply chain management, much information needs to be stored, processed, and transformed for decision making. To deal with the increasing amounts of data, firms must be aware of chances in supply chain management such as supply chain analytic capabilities to stay agile, flexible, and make use of (complex) data. Supply chain analytics can predict patterns and trends, even in high velocity markets in real-time supporting decision making by using supply chain analytic tools based on data. The benefits of successfully implementing supply chain analytic processes are enormous and result in competitive advantages for companies such as lowering costs while increasing revenues. As many companies fail to apply supply chain analytic processes and tools, this paper examines the challenges, benefits, and factors for the introduction of supply chain analytics using the input-output model

Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant

The replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbach, Germany. The plant includes an electrolyzer, a compressor, a low pressure storage tank, and six medium pressure storage tanks with complex heuristic physical coupling during the filling and extraction of hydrogen. Since these heuristics are too complex to be incorporated into the optimal control problem (OCP) explicitly, we propose a novel approach to do so implicitly. First, the MPC is executed without considering them. Then, the so-called allocator uses a heuristic model (of arbitrary complexity) to verify whether the MPC's plan is valid. If not, it introduces additional constraints to the MPC's OCP to implicitly respect the tanks' pressure levels. The MPC is executed again and the new plan is applied to the plant. Simulation results with real-world measurement data of the facility's energy management and realistic fueling scenarios show its advantages over rule-based control.Comment: 8 pages, 3 figures. To be published in IEEE 3rd International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE 2023) proceeding

Application of Pareto Optimization in an Economic Model Predictive Controlled Microgrid

This paper presents an economic model predictive control approach for a linear microgrid model. The microgrid in grid-connected mode represents a medium-sized company building including storage systems, renewable energies and couplings between the electrical and heat energy system. Economic model predictive control together with Pareto optimization is applied to find suitable compromises between two competing objectives, i. e. monetary costs and thermal comfort. Using real-world data from 2018 and 2019, the model is simulated with auto-detection of the Pareto solution which is closest to the Utopia point. The results show that the Pareto optimization can either be used in real-time control of the microgrid, or to obtain suitable weights from long term simulations. Both approaches result in significant cost reductions

Analyzing Information and Value Flows in High-Frequency Capital Markets

High-frequency trading has significant influence on today’s capital markets and has received massive attention in the media. This research aims to provide a conceptual understanding of high-frequency capital markets by analysing information and value flows between relevant high-frequency trading market participants. In a first step, market participants including traders, brokers, market platforms, technology providers, information providers, and clearing agencies are introduced. Second, the trading process is described focusing on the three most important phases, namely the information phase, order routing phase, and order matching phase. Furthermore, we review widely adopted high-frequency trading strategies such as market making, arbitrage trading, and pinging. Expert interviews are used to provide practical insights on the perception of high-frequency trading and the necessity for improved regulation. We merge theoretical knowledge and our findings from practice to develop the HFT Value Information Framework visualizing information and value flows between market participants. We discuss the interrelations between market participants in current high-frequency capital markets and describe implications for different stakeholders. Finally, the implications for regulatory bodies are discussed and possible future research opportunities are identified

Density functional theory using an optimized exchange-correlation potential

We have performed self-consistent calculations for first and second row atoms using a variant of density-functional theory, the optimized effective potential method, with an approximation due to Krieger, Li and Iafrate and a correlation-energy functional developed by Colle and Salvetti. The mean absolute deviation of first-row atomic ground-state energies from the exact non-relativistic values is 4.7 mH in our scheme, as compared to 4.5 mH in a recent configuration-interaction calculation. The proposed scheme is significantly more accurate than the conventional Kohn-Sham method while the numerical effort involved is about the same as for an ordinary Hartree-Fock calculation.Comment: To be published in Chemical Physics Letters (1995), latex, 15 pages, no figure