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Real-estate prices have soared since the start of the COVID-19 pandemic amid historically low mortgage rates and limited supply. The external shock observed in the real-estate market through relief measures may trigger changes in optimal conservation portfolios. The objective of this research is to identify the impacts of the real-estate market shock triggered by the pandemic through low-interest rates and limited real-estate supply on risk-diversification strategies for land conservation investment and to understand the implications of these impacts. We use a case study involving biodiversity conservation in the central and southern Appalachian region of the US by comparing modern portfolio theory (MPT) outputs using future conservation cost predictions with and without the shock. We find that the financial burden to fund the same level of risk-diversifying conservation benefits increases due to the shock and increases at an increasing rate as the return on investment (ROI) objective rises. This finding is alarming since higher conservation costs triggered by the shock decrease the cost-effectiveness of risk diversification, and this effect exacerbates with the goal of achieving higher ROI. Spatial diversification strategies with and without the shock offer risk-diversification information to help conservation organizations determine effective investment strategies. [ABSTRACT FROM AUTHOR]

The objective of our research is to extend current conservation applications of modern portfolio theory (MPT) to develop a framework for the cost-efficient budget distribution for a forest carbon payment program that optimizes risk-reward trade-offs in the presence of economic growth uncertainty over time. We consider correlation across space and time of the fluctuating opportunity costs of restoring forestland under changing future economic conditions using a case study of eight states in the central and southern Appalachian region of the United States. The findings suggest that optimal budget allocation decisions that ignore the covariance component of the spatial variance-covariance structure of forest carbon returns fail to minimize the true risk of conservation investment for any level of expected return. The importance of incorporating the spatial covariance in targeting conservation payments is made explicit through alternative approaches using multi-objective (mean-variance) optimization and an ex post analysis with and without the covariance component of the spatial variance-covariance structure of forest carbon return on investment (ROI). A comparison of these approaches against our MPT-based approach revealed misleading risk-return expectations if the ROI covariance is ignored in the spatial targeting of forest carbon payments under uncertainty. Key words: forest carbon payment, economic growth uncertainty, modern portfolio theory, return on investment, spatial variance-covariance. L'objectif de notre recherche est d'etendre les applications actuelles de la theorie moderne du portefeuille (TMP) dans le domaine de la conservation pour developper un cadre budgetaire rentable applique a un programme de paiement pour le carbone forestier qui optimise les compromis entre les risques et les recompenses dans un contexte d'incertitude de croissance economique, et ce, dans le temps. Nous considerons les correlations spatiale et temporelle de la fluctuation des couts d'opportunite de la restauration des terres forestieres dans des conditions economiques futures changeantes en utilisant une etude de cas pour huit etats des regions centrale et meridionale des Appalaches aux Etats-Unis. Les resultats indiquent que les decisions d'allocations budgetaires optimales qui ignorent la covariance en lien avec la structure de la variance-covariance spatiale des rendements du carbone forestier ne parviennent pas a minimiser le risque reel des investissements en conservation, et ce, pour tous les niveaux de rendement attendu. L'importance d'integrer la covariance spatiale dans le ciblage des paiements de conservation est explicite par le biais d'approches alternatives qui utilisent l'optimisation multi-objectifs (moyenne-variance). Une analyse ex post avec et sans la composante de covariance de la structure variance-covariance spatiale du retour sur investissement (RSI) du carbone forestier est aussi utilisee. Une comparaison de ces approches par rapport a notre approche basee sur la TMP a revele des previsions erronees en matiere de risque et de rendement si la covariance du RSI etait ignoree dans le ciblage spatial des paiements pour le carbone forestier dans un contexte d'incertitude. [Traduit par la Redaction] Mots-cles : paiement pour le carbone forestier, incertitude de la croissance economique, theorie moderne du portefeuille, retour sur investissement, variance-covariance spatiale., 1. Introduction Anthropogenic climate change, a consequence of long-standing increments in atmospheric greenhouse gas (GHG) concentrations, remains a major global issue (Boden et al. 2017). In the United States alone, [...]

This research determines the optimal distribution of firm-specific energy research and development (R&D) investment that balances firms' return and risk under market-induced uncertainty. We focus on creating optimal portfolios of target firms and their optimal energy R&D investments that maximize their return on investments (ROIs) for given levels of risk. We employ a stochastic optimization framework that maximizes firms' ROIs for energy R&D investment, measured by the ratio of the number of patents issued for energy technologies to the amount of annual energy R&D expenditures, for 78 energy firms in South Korea between 2006 and 2017. The findings from our mean-standard deviation tradeoff frontiers are summarized as follows: 1) the tradeoff ratio increases as the weights shift from 100% on maximizing expected ROI toward 100% on minimizing its standard deviation regardless of market conditions and 2) the tradeoff ratio during the downturn is higher than during the upturn. These findings suggest that firms mitigate market-induced risk with a smaller sacrifice in the expected number of issued patents when the initial weight is primarily on maximizing expected ROIs and when the market is experiencing an upturn instead of a downturn. From the distribution patterns of prioritized firms for the two extreme risk preference points along the upturn and downturn tradeoff frontiers, we find that the target firms shift under different market conditions and risk assumptions. These priority shifts highlight the importance of decision-maker flexibility in structuring firms' portfolios to support energy R&D, depending on the governments' risk tolerances and market conditions. [ABSTRACT FROM AUTHOR]

This study examines how the complementary relationships among multiple climate change adaptation strategies (improved varieties, irrigation practices and direct-seeded rice, referred to as 'DSR') are influenced by access to Extension services, training related to farming practices and weather-related information (referred to as 'institutional factors'). This study hypothesizes the influence of institutional factors and the resulting effects on their joint adoption using survey data collected from 359 rice producers in Chitwan district of Nepal in 2019. The results indicate: (1) adoption of all the three climate change adaptation practices are complementary to each other; (2) all three institutional factors enhance the likelihood of the adoption of variety, access to Extension and training enhances the likelihood of the adoption of improved irrigation, and information enhances the likelihood of the adoption of DSR and (3) access to Extension and training enhance the complementarities among all three pairs of adaptation practices (variety and irrigation; variety and DSR and irrigation and DSR), while information enhances the complementarity between variety and DSR only. The impact of complementary relationships among multiple climate change adaptation strategies influenced by the institutional factors is particularly valuable for policy makers and government agencies to allocate and prioritize resources. [ABSTRACT FROM AUTHOR]