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In this paper, the notion of Suzuki type generalized $\mathcal{L}$-contractions is introduced and a new fixed point theorem for such contractions is established.

SummaryConservation organizations often need to develop risk-diversification strategies that identify not just what species to protect but also where to protect them. The objective of this research is to identify optimal conservation investment allocations for both target sites and species under conditions of uncertainty. We develop a two-step approach using modern portfolio theory (MPT) to estimate percentages of conservation investment (referred to as ‘portfolio weights’) for counties and taxonomic groups in the central and southern Appalachian region under climate and market uncertainties. The portfolio weights across the counties and taxonomic groups from the two steps entail both spatial and taxonomic diversification strategies. Conservation decisions that allow for selecting sites for risk diversification fit the purpose of the first step. Likewise, conservation investments that benefit the biodiversity of particular taxonomic groups for the selected sites are made based on the relative importance of diversifying risk among species in a given area, fitting the purpose of the second step. The two-step MPT approach as a whole allows the greatest flexibility on where and what to protect for conservation investment under uncertainty, and thus would be applicable for the distribution of general conservation funds without predisposition towards protecting either specific sites or species.

To be able to protect biodiversity in coming decades, conservation strategies need to consider what sites will be important for species not just today but also in the future. Different methods have been proposed to identify places that will be important for species in the future. Two of the most frequently used methods, ecological niche modeling and climate resilience, have distinct aims. The former focuses on identifying the suitable environmental conditions for species, thus protecting the "actor", namely the species. The later seeks to safeguard the "stage", or the landscape in which species occur. We used the two methods to identify climate refugia for 258 forest vertebrates under short- and long- term climatic changes in a biodiversity hotspot, the Appalachian ecoregion of United States. We also evaluated the spatial congruence of the two approaches for a possible conservation application, that of protecting 30% of the Appalachian region, in line with recent national and international policy recommendations. We detected weak positive correlations between resilience scores and baseline vertebrate richness, estimated with ecological niche models for historical (baseline) climatic conditions. The correlations were stronger for amphibians and mammals than for birds and reptiles. Under climate change scenarios, the correlations between estimated vertebrate richness and resilience were also weakly positive; a positive correlation was detected only for amphibians. Locations with estimated future gain of suitable climatic conditions for vertebrates showed low correlation with resilience. Overall, our results indicate that climate resilience and ecological niche modeling approaches capture different characteristics of projected distributional changes of Appalachian vertebrates. Climate resilience (the stage) approach could be more effective in safeguarding species with low dispersal abilities whereas ecological niche modeling (the actor) approach could be more suitable for species with long-distance dispersal capacity as they may be more broadly impacted by climate and less sensitive to geophysical features captured by climate resilience approach. This article is protected by copyright. All rights reserved.

SummaryConsensus does not exist for which cost forms (i.e., one accounting solely for explicit cost and the other for both explicit and opportunity costs as in relative opportunity cost) are used in calculating return on investment (ROI) for conservation-related decisions. This research examines how the cost of conservation investment with and without inclusion of the opportunity cost of the protected area results in different solutions in a multi-objective optimization framework at the county level in the Central and Southern Appalachian Region of the USA. We maximize rates of ROI of both forest-dependent biodiversity and economic impact generated by forest-based payments for ecosystem services. We find that the conservation budget is optimally distributed more narrowly among counties that are more likely to be rural when the investment cost measure is relative opportunity cost than when it is explicit cost. We also find that the sacrifice in forest-dependent biodiversity per unit increase in economic impact is higher when investment cost is measured by relative opportunity cost rather than when measured by explicit cost. By understanding the consequences of using one cost measure over the other, a conservation agency can decide on which cost measure is more appropriate for informing the agency’s decision-making process.

Demographic changes have a profound impact on residential energy consumption. The number of single-person households is rapidly increasing around the world and the percentages of elderly individuals in the populations of almost all countries are expanding. The objective of our research was to analyze how single-person households and elderly households impact residential energy intensity, defined as annual residential energy consumption per capita per unit of finished area of the household’s house, and how those impacts interact with each other using South Korea as a case study. Our findings suggest that the rise of solo living and an ageing population have overlapping effects on energy consumption and threaten future improvements in residential energy intensity. Specifically, an increase of single-person households results in a decline in energy intensity regardless of whether the household is elderly or non-elderly and the effect of an increase in elderly households on energy intensity depends on whether the household is single- or multiple-person. Given the similar average size of finished area for single-person households, the difference in per unit energy consumption between elderly versus non-elderly households likely comes from behavioral differences such as a greater use of energy-intensive appliances by non-elderly households than elderly households. However, for multiple-person households, the effect of such behavioral differences seems to be dominated by the effect of a house’s shared amenities. The common space and energy-consuming amenities of a house are shared by more individuals in non-elderly households, leading to more intensive energy consumption by non-elderly multiple-person households than by elderly multiple-person households.

The role of time in estimating the cost of forest carbon is often ignored in the literature, nor does the literature address the issues of where and when the purchase of forest carbon storage becomes socially beneficial. In our study, we identify the spatial and temporal allocations of forest carbon investments that are socially beneficial based on empirical analysis. We use the Central and Southern Appalachian region in the Eastern United States as a case study over three periods (i.e., 1992–2001, 2001–2006, and 2006–2011) that are roughly in line with moderate, upturn, and downturn market conditions. The areas from which it is socially beneficial to buy carbon storage are mainly in flat terrain and further away from urban boundaries, hence facing lower development pressure and lower urban net returns. These areas also have less urban land and more forestland. The mapping of carbon cost over the three market conditions in our case study also indicates that the socially beneficial carbon area shrinks as the opportunity cost increases when the real-estate market evolves from a moderately growing to a booming market. The socially beneficial carbon area shrinks further as the demand from urban development on forestland collapses when the real-estate market enters a downturn stage.

The United States (U.S.) road freight sector has continued to grow over recent decades. Growth in road freight could result in more fuel consumption and hence increased greenhouse gas emissions. Policymakers have attempted to manage the growth of energy usage through improved fuel economy based on technological advances. However, such improvements may not lead to anticipated goals because of the rebound effect, where improvements in energy efficiency trigger more travel and energy consumption that offsets energy savings. Thus, this study aims to determine the potential rebound effect from improved energy efficiency in the U.S. road freight sector. Eight fuel cost models are applied and asymmetric price response is incorporated in estimating the U.S. road freight sector’s rebound effect from 1980 to 2016. In addition, a recently developed data envelopment analysis is applied to determine the annual rebound effect in the road freight sector. The results suggest that, after accounting for the asymmetric price response, the average rebound effect of the U.S. road freight sector ranges from 6.9% to 8.8%, a level considerably less than that found for several industrialized countries and emerging economies. However, a considerable increase in the rebound effect has been seen in more recent years. The findings suggest that overlooking the rebound effect in environmental policies could impede the goal of reducing total energy consumption and accompanying emissions. Policymakers should incorporate the rebound effect from efficiency enhancement in policy development and utilize some potential programs to reduce the adverse influence of rebound effect in related policies.

We analyze how the optimal spatial budget distribution for protecting ecosystem services under two extreme market conditions results in different ecological–economic tradeoffs for balance between conservation and sustainable development. As a case study, we develop an empirical framework for the optimal spatial budget distribution given the objectives of maximizing forest carbon storage and maximizing total value added in the Central and Southern Appalachian Region. We consistently find concave efficient frontiers between carbon storage and total value added and differences in ecological–economic tradeoffs under two extreme market conditions in 2006 and 2011 for intra- and inter-generational sustainability. The former confirms previous findings of a concave tradeoff relationship, while the latter new finding suggests that balancing weights between the two objectives with preferences of decision makers can be effectively done depending on the market conditions. For example, if a conservation agency considers increasing the weight on either maximizing total value-added or maximizing forest carbon storage, the decision makers should consider the sacrifice of the other objective required by the optimal decision, which changes across market conditions. We also find that a conservation agency may want to consider the negative consequences on equity between rural and urban areas of increasing the weight on maximizing total value-added, regardless of the market condition.

We analyze optimal budget allocations to acquire protected areas for carbon storage while balancing risk and return from protection under economic growth uncertainty in a local community. Our study is the first to explore how risk of uncertain economic growth affects cost of protected area acquisition using real estate values at the parcel level, enabling us to estimate the site-specific opportunity cost of carbon storage. The Pareto optimal trade-off frontier between the expected carbon storage benefit and its variance provides a continuum of risk-return combinations. The pattern of the trade-off relationship implies that risk mitigation is less costly in terms of foregone expected benefit when risk is higher than when it is lower. Our results also find that the difference in cluster-specific budget allocations between the strong economic growth scenario and the weak economic growth scenario subsequently decreases between the point of expected benefit maximization and the point of variance minimization. Our findings of optimal hectares of land for protected area acquisition for carbon storage and corresponding benefits and costs serve as an empirically informed knowledge base to help a local community prioritize acquisition of potential protected areas for carbon storage under economic growth uncertainty.

This paper investigates the uncertainty–research and development (R&D) investment relationship and its interactions with firm size by allowing flexibility in the relationships among uncertainty, R&D investment, and firm size. We hypothesize that the uncertainty effect on R&D investment varies by firm size using South Korean firm-level data. Firm-level uncertainty is measured by variation in a firm’s sales revenue, and firm size is measured by two proxies: the firm’s sales revenue and the firm’s number of employees. We find a concave relationship between the uncertainty elasticity of R&D investment and firm size using separate models for the two firm size proxies. The concave relationship is explained by a change in the dominance of the call option effect, then the put option effect, and then again the call option effect as firm size increases. This relationship serves as an empirically informed knowledge base for policymakers to utilize in evaluating government-funded R&D contracts for encouraging R&D investment.

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.

We determine the optimal spatial budget distribution of forest carbon payments that balances the returns and risks associated with conservation costs (opportunity cost of forestland) affected by future economic growth scenarios using a case study of the central and southern Appalachian region of the USA. A further focus is to evaluate the county-level tradeoffs between the returns and risks of future economic growth that affect the expected benefits and variance of forest carbon storage by constructing a mean–variance tradeoff frontier. Because of concavity of the mean–variance tradeoff frontier, mitigating risk by dispersing budget allocations among counties is advisable, particularly if conservation agencies focus on the returns with little or no regard for the risks associated with future growth at the initial policy-making stage. The different dispersions of the budget among counties for different weights placed on risk minimization provide clear evidence that spatial targeting for conservation and restoration investments such as forest carbon payments needs to consider the risk preferences of conservation agencies regarding conservation costs. Our findings suggest that failing to anticipate the potential risks will lead to suboptimal conservation targets and budget allocations, if conservation agencies are risk averse with little or no regard for the return-maximizing objective.

The objective of this research is to examine how protected area size influences the conservation benefit and acquisition cost of creating a protected area, how the resulting effects influence the predicted rate of return on investment (ROI), and how those relationships change prioritization decision-making for selecting protected areas compared with decisions based only on conservation benefit and decisions based only on acquisition cost. The objective is accomplished in an econometric framework by analyzing the parcel-level acquisition cost and conservation benefit measured by the change in potential fragmentation patterns on the landscape resulting from protection. We focus on areas acquired by The Nature Conservancy in central and southern Appalachia, United States. As an indicator of the change in landscape fragmentation, we use a fragmentation statistic known as effective mesh size. Although the effect of protected parcel size on predicted ROI is inelastic, greater conservation effectiveness is obtained with larger protected parcels than with smaller ones on average. Protected parcel size influences parcels' rankings for protection more (less) when only the predicted change in effective mesh size of protected area (only the predicted acquisition cost per area) is used for prioritizing parcels than when the ranking of parcels is determined by the predicted ROI. These findings imply that, although protected parcel size is important, failure to prioritize using ROI could result in an inappropriate level of emphasis being given to protected parcel size than is warranted.

We identify optimal spatial targets for payments for forest carbon sequestration under the multiple objectives of maximizing forest carbon storage cost efficiency and maximizing economic impacts of payments. A further purpose is to evaluate the tradeoff between the two objectives. These objectives are used as targeting criteria in our case study of the Central and Southern Appalachian Region of the United States, a heavily forested, low-income region that could benefit from payments for forest carbon sequestration. The concave-shaped Pareto optimal frontiers between forest carbon storage and economic impacts triggered by payments provide evidence that the increase in economic impacts is relatively higher than the sacrifice in forest carbon benefits when the initial weight assigned to economic impacts is relatively lower than the initial weight assigned to forest carbon benefits and vice versa. Our projections of county-level forest carbon storage and economic impacts can help conservation agencies anticipate regional heterogeneity in forest carbon storage and economic impacts and access their tradeoffs.

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.

Spatial consideration of costs and benefits plays a critical role in assessing the effectiveness of payments for ecosystem services (PES). While spatial assessment has received much attention, few, if any, studies have explicitly considered spatial variations in the benefits and landowners’ opportunity costs of competing land uses as targeting criteria for PES. The objective of our research is to identify different spatial targets for PES based on spatial variations in ecosystem benefits and opportunity costs for competing land uses. As a case study, we use incentive payments for forest carbon storage in the Central and Southern Appalachian Regions of the eastern United States. We find, on average, supplying forest carbon storage by converting pasture to forest is approximately five times more cost effective than mitigating deforestation for urban use because of its lower opportunity cost and its higher per hectare gain in carbon storage. We also find that the targeted areas that have positive net social benefits in supplying forest carbon represent 9.32% of the case-study region’s pasture land, while zero pixels are identified with positive net social benefits when urban use is the competing land use. These findings imply that the spatial targeting of the region’s areas that have positive net social benefits should focus on afforesting pasture instead of preventing forestland from being converted to urban use. The results also help target cost-effective areas for afforestation of pasture for carbon storage.

Policy guidelines for creating new protected areas commonly recommend larger protected areas be favored. We examine whether these recommendations are justified, providing the first evaluation of this question to use return-on-investment (ROI) methods that account for how protected area size influences multiple ecological benefits and the economic costs of protection. We examine areas acquired to protect forested ecosystems in the eastern US that are rich in endemic species. ROI analyses often alter recommendations about protected area size from those obtained when considering only ecological benefits or only economic costs. Large protected areas offer a greater ecological return per dollar invested if the goal of protecting sites is to reduce forest fragmentation on the wider landscape, whereas smaller sites offer a higher ROI when prioritizing sites offering protection to more species. A portfolio of site sizes may need to be included in protected area networks when multiple objectives motivate conservation.

This research analyzes the effects of market conditions on the performance of incentive payment approaches for forest-based carbon sequestration. We develop supply curves for sequestered carbon using the relationship between deforestation for urbanization and the relative returns from forest products and urban uses under two different market conditions. The empirical results for an 18-county case study show that a hypothetical payment system was effective and the marginal cost of carbon sequestration increased with the target level of carbon sequestration during the 2001–2006 real estate upturn, while the same system was ineffective during the 2006–2011 period that included a real estate downturn. Our study is the first to examine the role of temporal changes in market conditions on the performance of incentive payment approaches. Although a first step, our snapshot, static approach shows the value in thinking of the dynamic role of changing market conditions in evaluating incentive payment systems.

A growing body of literature suggests a need to incorporate both complementary and competitive relationships among multiple objectives into conservation investment decisions. To do this, we hypothesize that spatial budget allocations and overall benefits for payment for ecosystem services (PES) are influenced by the complexity of complementary and competitive relationships among multiple objectives of PES, i.e., maximizing forest-dependent biodiversity (or “biodiversity”), forest-based carbon storage (or “carbon”), and economic impact triggered by PES. To verify the hypothesis, we apply the multi-objective optimization framework to 231 counties in eight states of the Central and Southern Appalachian Region of the United States. We find 1) narrower spatial budget allocations with inclusion of the objective of maximizing economic impact, which has competitive relationships with the existing complementary biodiversity and carbon objectives, and 2) the foregone overall benefits from the existing complementary objectives increase with further increases in the competitive economic-impact objective. These findings imply that conservation agencies involved in PES planning should be cautious about the negative consequences on distributional equity found in 1) above, and the increasing sacrifice in the existing complementary objectives found in 2) above, when considering introduction of a new economic objective that has a competitive relationship with the existing ecological objectives.

We analyze how two different ecosystem services: forest-based amenity values and carbon storage benefits, affect spatial targeting for conservation investment for protected area acquisition using selected forest clusters in Knox County, Tennessee in the United States. We determine return-on-investment (ROI) for these two different forest-based ecosystem services by estimating amenity values and carbon storage amounts using hedonic price model and dynamic Terrestrial Ecosystem Model (TEM), respectively, and corresponding acquisition costs at the forest cluster level. Our findings of the optimal protected area acquisition in the form of forest clusters serve as an empirically informed knowledge base to help both the conservation and planning agencies in prioritizing acquisition of potential protected areas depending on their preferences. By using carbon and amenity ROIs in spatial targeting of forest clusters within the multi-objective optimization set up, we not only addressed the spatial heterogeneity in the carbon storage benefits and amenity values but also the spatial heterogeneity in cluster acquisition costs. We also found that selection decisions were dictated by the weakly negative correlation (−0.16) between the carbon and amenity ROIs instead of weakly positive correlation (0.14) between carbon storage benefits and amenity values. Since the spatial distribution of carbon and amenity ROIs were weakly negatively correlated, there were apparent conflicts between the objectives of maximizing forest carbon storage and amenity value. This resulted in concave frontiers with tradeoffs between the objectives implying variation in spatial distribution of the selected forest clusters letting the conservation and planning agencies decide combination of strategies which best fit their preferences.

This study investigates the effects of free trade agreements (FTAs) on trade in transport services using OECD data from 2003 to 2006. Our analysis found that FTAs had a positive overall impact on transport services for multiple countries (i.e., 26 home and 56 partner countries). The resulting positive overall impact assures that, even with the challenges associated with different layers of services and the obstacles formed by generally low trade openness in the sector, the provisions in FTAs (e.g., national treatment and market access for goods and services) promote transport service trades. Our findings suggest that the provisions in FTAs encourage economic agents to increase engagement in transport services because of expanded openness of the physical movement of goods across international borders.

SUMMARYForest carbon sequestration plays an important role in reducing the build-up of greenhouse gases that are known to contribute to global climate change. However, private landowners will supply less carbon sequestration than would be socially desirable if they are unable to capture the economic value of sequestration. We examine the viability of offering landowners property tax subsidies for forest carbon sequestration (referred to as a ‘tax-based subsidy approach’). Waiving property taxes on forestland provides incentives for landowners to afforest non-forested land and/or sustain forests that are at risk of deforestation. We focus on 17 Tennessee counties and one Kentucky county, constituting one of 179 Bureau of Economic Analysis areas in the United States, as a case study. Higher forestland net return from waiving property taxes increases the share of forestland in the 18 counties, which in turn increases the accumulation of carbon in the forest ecosystem, suggesting that this is a viable approach. The annualized county-level cost of supplying forest carbon sequestration using a tax-based subsidy ranges between US$15.56 and US$563.58 per carbon tonne across the 18 counties. Relevant government agencies can use these estimates to target selected counties for more cost-effective adoption of the county-level tax-based subsidy approach.

In this paper, the notion of generalized set-valued weak θ-contractions is introduced and a new fixed point theorem for such contractions is established in the setting metric spaces. The main result is a generalization of fixed point theorems in the literature. An example and an application to generalized differential equation are given to support the validity of the main theorem.

The role of time in estimating the cost of forest carbon is often ignored in the literature, nor does the literature address the issues of where and when the purchase of forest carbon storage becomes socially beneficial. In our study, we identify the spatial and temporal allocations of forest carbon investments that are socially beneficial based on empirical analysis. We use the Central and Southern Appalachian region in the Eastern United States as a case study over three periods (i.e., 1992-2001, 2001-2006, and 2006-2011) that are roughly in line with moderate, upturn, and downturn market conditions. The areas from which it is socially beneficial to buy carbon storage are mainly in flat terrain and further away from urban boundaries, hence facing lower development pressure and lower urban net returns. These areas also have less urban land and more forestland. The mapping of carbon cost over the three market conditions in our case study also indicates that the socially beneficial carbon area shrinks as the opportunity cost increases when the real-estate market evolves from a moderately growing to a booming market. The socially beneficial carbon area shrinks further as the demand from urban development on forestland collapses when the real-estate market enters a downturn stage.