Systematic conservation planning (SCP) deals with a delicate interplay of competing interests and has far-reaching impacts for all stakeholders and systems involved. While SCP has traditionally attempted to conserve ecosystem services that benefit ecological systems, public perceptions of conservation initiatives influence their ultimate feasibility and sustainability. In an attempt to balance ecological integrity, social utility, and urban development, this study develops a framework that applies four popular models to represent these competing factors, including two ecosystem services models-InVEST (Integrated Valuation of Environmental Services and Tradeoffs) for biophysical services (BpS), and SolVES (Social Values for Ecosystem Services) for social values (SV); a land use and land cover (LULC) suitability model; and Zonation for delimiting high priority areas. We also analyze a number of conservation scenarios that consider varying levels of urban development. While BpS are distributed with considerable spatial variability, SV spatially overlap. Approximately 6% of the area was identified as having both high BpS and SV, whereas a further 24.5% of the area was identified as either high BpS low SV or vise-versa. Urban development scenarios affected the conservation area selection drastically. These results indicate tradeoffs and potential synergies between development, SV, and BpS. Our findings suggest that the information provided by the proposed framework can assist in finding solutions to social-ecological planning complexities that serve multiple stakeholders.
Identifying relevant location determinants is a good starting point for shop operators, help to increase profitability and, thus, avoiding business failure. Traditional Analytic Hierarchy Process (AHP) or the Analytic Network Process (ANP) have shortages that require improvement. Herein, Decision-Making Trial and Evaluation Laboratory (DEMATEL), ANP based on DEMATEL (DANP), and modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (modified VIKOR) are used to construct a hybrid multiple-attribute decision making (MADM) model, encompassing three dimensions and thirteen criteria in exploring the location determinants of Asia’s unique Bubble Tea Shops (BTSs) and to evaluate three preselected alternatives in Nanjing, China. The empirical findings of the DEMATEL method reveal that traffic traits (D1) and site traits (D2) are critical to BTSs, and that once these are enhanced, shop traits (D3) are also improved. Criteria deemed as important, based on the DEMATEL and DANP methodology, are (in descending order): proximity to a street corner (C2), proximity to public transportation systems (C1), road width (C3), proximity to communities (C5), proximity to commercial areas (C6), types of shop (C9), and proximity to schools (C7). Different decision-making rankings among alternatives are indicated based upon the modified VIKOR method and corresponding strategies for improvement are presented.
This study analyzes affine styled-facts price dynamics of Henry Hub natural gas price by incorporating the price features of jump risk, and seasonality within stochastic volatility framework. Affine styled-facts dynamics has the advantage of being able to incorporate mean reversion (MR), stochastic volatility (SV), seasonality trends (S), and jump diffusion (J) in a standardized inclusive framework. Our main finding is that models that incorporate jumps significantly improve overall out-of-sample option pricing performance. The combined MRSVJS model provides the best fit of both daily gas price returns and the related cross section of option prices. Incorporating seasonal effects tend to provide more stable pricing ability, especially for the long-term option contracts.
This study extends the mean‐reversion dynamic framework of (Pilipovic, Energy risk: Valuing and managing energy derivatives, 1997) and (Schwartz, The stochastic behavior of commodity prices: Implications for pricing and hedging, Journal of Finance52, 1997, 923) and focuses on developing a variety of continuous‐time commodity‐pricing and hedging models by analyzing the pricing and hedging errors found in an empirical investigation of options contracts on light sweet crude oil traded on the New York Mercantile Exchange. Thus, this study contributes to furthering the applicability of the models developed. The inclusion of the benchmark Black‐Scholes pricing model generates systematic biases that are consistent with (Bakshi, Cao and Chen, Handbook of Quantitative Finance and Risk Management, 2010). The mean‐reversion jump‐diffusion and seasonality option‐pricing model best describes the extreme price volatility experienced during a financial collapse, but the mean‐reversion and seasonality option‐pricing model offers the best pricing and hedging capability for other periods. The performances of hedging models are generally consistent with pricing errors.
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