Property prices and the value of amenities

Implicit hedonic prices of amenities are estimated by means of a study of the variation in house prices in Sydney. The results include estimated hedonic prices for aircraft noise, road traffic, road widening, good views, spacious streets, good access to shops, and high quality neighborhoods. Since hedonic prices are the response of the market at the margin to supply as well as demand forces, they do not necessarily reflect average household willingness to pay prices. However, the paper concludes that they provide a basis from which the values of amenities to be used in benefit cost studies can be estimated.     

How economics can further the success of ecological restoration

Restoration scientists and practitioners have recently begun to include economic and social aspects in the design and investment decisions for restoration projects. With few exceptions, ecological restoration studies that include economics focus solely on evaluating costs of restoration projects. However, economic principles, tools, and instruments can be applied to a range of other factors that affect project success. We considered the relevance of applying economics to address 4 key challenges of ecological restoration: assessing social and economic benefits, estimating overall costs, project prioritization and selection, and long‐term financing of restoration programs. We found it is uncommon to consider all types of benefits (such as nonmarket values) and costs (such as transaction costs) in restoration programs. Total benefit of a restoration project can be estimated using market prices and various nonmarket valuation techniques. Total cost of a project can be estimated using methods based on property or land‐sale prices, such as hedonic pricing method and organizational surveys. Securing continuous (or long‐term) funding is also vital to accomplishing restoration goals and can be achieved by establishing synergy with existing programs, public–private partnerships, and financing through taxation.     

Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities?

The hedonic property value model is among our foremost tools for evaluating the economic consequences of policies that target the supply of local public goods, environmental services, and urban amenities. We design a theoretically consistent and empirically realistic Monte Carlo study of whether omitted variables seriously undermine the method’s ability to accurately identify economic values. Our results suggest that large gains in accuracy can be realized by moving from the standard linear specifications for the price function to a more flexible framework that uses a combination of spatial fixed effects, quasi-experimental identification, and temporal controls for housing market adjustment.     

The value of disappearing beaches: A hedonic pricing model with endogenous beach width

Beach nourishment is a policy used to rebuild eroding beaches with sand dredged from other locations. Previous studies indicate that beach width positively affects coastal property values, but these studies ignore the dynamic features of beaches and the feedback that nourishment has on shoreline retreat. We correct for the resulting attenuation and endogeneity bias in a hedonic property value model by instrumenting for beach width using spatially varying coastal geological features. We find that the beach width coefficient is nearly five times larger than the OLS estimate, suggesting that beach width is a much larger portion of property value than previously thought. We use the empirical results to parameterize a dynamic optimization model of beach nourishment decisions and show that the predicted interval between nourishment projects is closer to what we observe in the data when we use the estimate from the instrumental variables model rather than OLS. As coastal communities adapt to climate change, we find that the long-term net value of coastal residential property can fall by as much as 52% when erosion rate triples and cost of nourishment sand quadruples.     

Intergenerational equity: The capital-environment trade-off

This paper examines the trade-off between capital accumulation and environmental quality that determines an intergenerationally equitable program according to the Rawls maximin criterion. This trade-off is explored by an analysis of regular maximin programs in the framework developed by Brock. This results in a constant utility path supported by competitive prices with government imposed effluent charges and environmental rentals. Sufficient conditions for a regular maximin path to satisfy a Hartwick rule in deciding on the combination of capital and environmental quality left to future generations are given.     

Sustainable development: the challenge of implementation

This paper focuses on efforts to produce an operational definition of the concept of ‘sustainable development’ as articulated by the 1992 United Nations Conference on Environment and Development (UNCED). ‘Sustainability’ has become an increasingly significant environmental issue; the problems in articulating a workable concept will differ among nations and international organizations attempting to define the term. Additionally, the term encompasses the varied fields of ecology, philosophy, and economics; every discipline imparts its own bias. Various definitions are provided and the significance and difficulty of developing an operational definition of Sustainable development is discussed. For example, sustainable development has become a ‘needs'-oriented term, an entitlement that priority should be given to the needs of the world's poor. Some argue that living standards which go beyond the basic minimum are sustainable only if consumption standards everywhere have regard for long-term sustainability. Scientifically, sustainability involves replicability and regeneration through an unforeseeable future. When prices reflect social cost and there are no externalities, optimal choices will be made between present and future consumption. Much of the debate involving sustainability involves deep-rooted fears or phobias, e.g. resource depletion, ‘energy crises’ and ‘timber crises’.     

Measuring the benefits of air quality improvements: A hedonic salary approach

This paper estimates the compensating variation associated with nonmarginal changes in air quality using a hedonic salary model and 1970 data collected for a national sample of university professors. Recent advances in the theory of hedonic prices are utilized in constructing the theoretical model and formulating a procedure for generating empirical estimates. Lower bound estimates of the compensating variation associated with a one standard deviation increase in total suspended particulates (27.6 mgs/cu. meter/24 hours) were $419 for full professors, $234 for associate professors, and $209 for assistant professors in 1970. These results were not sensitive to the specification of the hedonic salary equation.     

Is energy efficiency capitalized into home prices? Evidence from three U.S. cities

We test for evidence that energy efficiency features are capitalized into home prices in three U.S. metropolitan areas. Using hedonic regressions and multiple matching procedures, we find that Energy Star certification is associated with higher sales prices in two of the markets: the Research Triangle region of North Carolina and Portland, Oregon. We find that local “green” certifications in Portland and in Austin, Texas, are also associated with higher prices and that the estimated price impacts are larger than those from Energy Star. Matching on observables proves to be important in some cases, reducing the estimated impacts compared with models without matching. We calculate the implied energy savings from the estimated premiums and find that, in the Research Triangle market, the Energy Star premiums approximately equal the savings that program is designed to achieve, but in Portland, the premiums are slightly greater than the program's savings due to low energy costs in the region.     

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.     

Spatial aggregation and the value of natural capital

Location matters for the value of capital assets. The value of changes in natural capital wealth can depend on whether natural capital asset prices are measured locally and then aggregated or whether average values are applied over aggregate representative areas. Spatial heterogeneity of resource characteristics and institutions impact approximations of the intertemporal welfare function and accounting price function because when spatial aggregation precedes valuation it implies greater arbitrage opportunities leading to more inelastic shadow (accounting) price functions than when valuation is done locally and then aggregated. Aggregation of observed values across varying resource and institutional characteristics can lead to omitted variables bias. We illustrate these results in the context of groundwater in the Kansas High Plains Aquifer and demonstrate that the accounting price function is less elastic when the accounting price is measured locally. Failure to measure locally and then aggregate could lead to undervaluing scarce resources and overvaluing plentiful ones, which biases wealth accounts in favor of passing the non-declining wealth sustainability test.     

Forecasting industrial emissions: a monetary approach vs. a physical approach

Forecasts of industrial emissions provide a basis for impact assessment and development planning. To date, most studies have assumed that industrial emissions are simply coupled to production value at a given stage of technical progress. It has been argued that the monetary method tends to overestimate pollution loads because it is highly influenced by market prices and fails to address spatial development schemes. This article develops a land use-based environmental performance index (L-EPI) that treats the industrial land areas as a dependent variable for pollution emissions. The basic assumption of the method is that at a planning level, industrial land use change can represent the change in industrial structure and production yield. This physical metric provides a connection between the state-of-the-art and potential impacts of future development and thus avoids the intrinsic pitfalls of the industrial Gross Domestic Product-based approach. Both methods were applied to examine future industrial emissions at the planning area of Dalian Municipality, North-west China, under a development scheme provided by the urban master plan. The results suggested that the LEPI method is highly reliable and applicable for the estimation and explanation of the spatial variation associated with industrial emissions.     

Remote sensing: Using the spatial domain

Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.     

Algorithms and biplots for double constrained correspondence analysis

Correspondence analysis with linear external constraints on both the rows and the columns has been mentioned in the ecological literature, but lacks full mathematical treatment and easily available algorithms and software. This paper fills this gap by defining the method as maximizing the fourth-corner correlation between linear combinations, by providing novel algorithms, which demonstrate relationships with related methods, and by making a detailed study of possible biplots and associated approximations. The method is illustrated using ecological data on the abundances of species in sites and where the species are characterized by traits and sites by environmental variables. The trait data and environment data form the external constraints and the question is which traits and environmental variables are associated, how these associations drive species abundances and how they can be displayed in biplots. With microbiome data becoming widely available, these and related multivariate methods deserve more study as they might be routinely used in the future.     

Mapping water chemical variables with spatially correlated errors

One of the most important considerations in many environmental studies is need to allow for correlations among the variables. Monitoring and analyzing relationships between chemical environmental parameters using spatial correlation based regression modelling is the main motivation of this applied study. For this purpose, some noticeable environmental parameters of data sets obtained from two lakes have been considered and the concentrations of chemical variables such as cadmium and nitrate have been appraised by a regression-based geostatistical methodology. The modelling procedure consists of two stages. In the first stage, spatial variables are analyzed via multi-linear regression and some relationships are provided. Next, by using the spatial auto-correlations of the residuals, a type of regression-based kriging procedure is applied. The capacity of the model for appraising the water chemical variables is also tested and performance comparisons with ordinary kriging are conducted. Finally, the applications showed that analyzing water chemical variables with spatially correlated errors is a convenient and applicable approach for assessing the environmental systems.     

Male-biased sex ratios in New Zealand fur seal pups relative to environmental variation

A number of models have been proposed to provide adaptive explanations of sex-ratio variation in mammals. Two models have been applied commonly to primates and ungulates with varying success—the Trivers-Willard (TW) hypothesis, and the local resource competition (LRC) hypothesis. For polygynous, sexually dimorphic mammals, where males are larger and disperse more readily, these models predict opposite outcomes of sex-ratio adjustment within the same environmental context (high-resource years: TW—more sons; LRC—more daughters). However, many of the predictions of these two models can vary depending on factors influencing resource availability, such as environmental stochasticity, resource predictability, and population density. The New Zealand fur seal (Arctocephalus forsteri) is a polygynous mammal showing marked sexual dimorphism (larger males), with higher variation in male reproductive success expected. We provide clear evidence of male-biased sex ratios from a large sample of A. forsteri pups captured around South Island, New Zealand during 1996/1998, even after accounting for a sex bias in capture probability. The extent of the bias depended upon year and, in 1998, strong climatic perturbations (El Niño/Southern Oscillation, ENSO) probably reduced food availability. Significant male-biased sex ratios were found in all years; however, there was a significant decline in the male bias in 1998. There was no relationship between sex ratio and population density. We suggest that the sex-ratio bias resulted from the production of relatively more male pups. Under the density-independent scenario, the strong male bias in A. forsteri sex ratios is support for the TW model within an environment of high resource predictability. We suggest that some plasticity in the determination of pup sex among years is a mechanism by which A. forsteri females in New Zealand, and perhaps other otariid seals, can maximise fitness benefits when living in regions of high, yet apparently predictable, environmental variability. We also suggest that much of the inconsistency in the reported sex ratios for otariid seals results from the complex interaction of population density and environmental stochasticity influencing relative food availability over time.     

Exhaustible Resource Allocation in an Overlapping Generations Economy

This paper analyzes competitive allocations of an exhaustible resource in an overlapping generations economy. Conditions are given for the existence of equilibrium extraction rules such that both extraction and investment in future stocks are increasing and continuous in the current stock. The paper then considers the time paths of equilibrium allocations and shows that there are economies where equilibrium resource extractions and prices exhibit nonclassical behavior. This is illustrated through a finite horizon example in which extractions increase and prices decrease over the entire time horizon and an infinite horizon example where there are persistent cycles in extractions and prices and multiple equilibria. The paper concludes by examining welfare issues.     

Competitive investment in clean technology and uninformed green consumers

In a market where consumers and the regulatory authorities are not fully informed about the actual production technology or environmental performance of firms that engage in strategic competition, I study the effect of environmental consciousness of consumers on firms׳ incentive to invest in cleaner technology. Firms compete in prices and may signal their environmental performance to uninformed consumers through prices. I also analyze the effect of an expected liability on firms in this setting. Compared to full information, incomplete information generates higher strategic incentive to invest in cleaner technology particularly when consciousness and/or expected liability are not too high. Requiring mandatory disclosure of technology or environmental performance may discourage such investment. Even though consumers and the regulator are uninformed, competition has a positive effect (relative to monopoly) on the incentive to invest.     

城市环境影响模拟的系统动力学研究

城市化进程的加快已导致严重的环境问题。对城市环境影响的模拟研究是确定合理的城市发展规模和制定科学的发展规划的重要依据,以采取措施从源头控制环境质量的恶化。城市环境属于复合多动态系统,传统的一些方法可以实现城市环境影响的模拟预测,但参数调控方面的功能较不完善。文章将基于信息反馈控制理论的系统动力学引入城市环境影响模拟中,构建城市环境、社会、经济之间的系统动力学模型,通过调整环保投入、科技投入、经济增长速率以及单位能耗等系统变量,对武汉市未来发展的主要污染物（SO2、COD）排放量和主要资源消耗量（能源、水）等进行了四种情景动态模拟。结果表明：（1）系统动力学适合于研究复杂系统行为,具有较强的模拟和调控能力,同时可以避免繁琐的计算和信息成本高等缺点;（2）＂两型＂发展模式（Ⅳ）下的资源消耗量和污染物排放量明显低于其它3种情景,但仍处于较高的水平。武汉市污染物削减、生态环境保护任务依然繁重,开发新的替代能源、提高资源利用率仍是今后工作的重点。     

Variation partitioning of species data matrices: estimation and comparison of fractions

Establishing relationships between species distributions and environmental characteristics is a major goal in the search for forces driving species distributions. Canonical ordinations such as redundancy analysis and canonical correspondence analysis are invaluable tools for modeling communities through environmental predictors. They provide the means for conducting direct explanatory analysis in which the association among species can be studied according to their common and unique relationships with the environmental variables and other sets of predictors of interest, such as spatial variables. Variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure. Although variation partitioning in canonical analysis is routinely used in ecological analysis, no effort has been reported in the literature to consider appropriate estimators so that comparisons between fractions or, eventually, between different canonical models are meaningful. In this paper, we show that variation partitioning as currently applied in canonical analysis is biased. We present appropriate unbiased estimators. In addition, we outline a statistical test to compare fractions in canonical analysis. The question addressed by the test is whether two fractions of variation are significantly different from each other. Such assessment provides an important step toward attaining an understanding of the factors patterning community structure. The test is shown to have correct Type I. error rates and good power for both redundancy analysis and canonical correspondence analysis.     

Ease vs. noise: Long-run changes in the value of transport (dis)amenities

For a complete cost-benefit analysis of durable infrastructures, it is important to understand how the value of non-market goods such as transit time and environmental quality changes as incomes rise in the long-run. We use difference-in-differences and spatial differencing to estimate the land price capitalization effects of metro rail in Berlin, Germany today and a century ago. Over this period, the negative implicit hedonic price of rail noise tripled. Our results imply income elasticities of the value of noise reduction and transport access of 2.2 and 1.4, substantially exceeding cross-sectional contingent valuation estimates.