Long-term impacts of exposure to high temperatures on human capital and economic productivity

Weather anomalies have a range of adverse contemporaneous impacts on health and socio-economic outcomes. This paper tests if temperature anomalies around the time of birth can have long-term impacts on individuals' economic productivity. Using unique data sets on historical weather and earnings, place and date of birth of all 1.5 million formal employees in Ecuador, we find that individuals who have experienced in-utero temperatures that are 1 °C above average are less educated and earn about 0.7% less as adults. Results are robust to alternative specifications and falsification tests and suggest that warming may have already caused adverse long-term economic impacts.     

The growth response of a model Gymnodinium splendens in stationary and wavy water columns

A computer model is used to investigate the simulated growth of a theoretical dinoflagellate resembling Gymnodinium splendens in response to a variety of field conditions. Literature data on G. splendens are combined with probable estimates of organism response (where direct data are lacking) to yield light-and temperature-dependent production curves. These production curves are superimposed on a physical model characterized by a diurnally variable light cycle, by a two-layered water column (16°C water overlaying 12°C water) of variable layer thicknesses, and by variable extinction coefficients in the upper layer. The water column is either stationary or perturbed by a semidiurnal (12.4 h) internal wave. Organism behavior ranges from the continuous occupation of selected strata (stationary or wavy) to diurnal vertical migrations within the upper layer or across the thermocline. In stationary water columns, species patchiness depends on spatial differences in the depth preferences of nonmigrating organisms or in the details of the behavior of migrating organisms. In water columns perturbed by a semidiurnal internal wave, spatial differences in the phase relationship between the wave form and daylight supplement organism behavior as a source of patchiness. The models result in their most complex spatial patterns when a population migrates through a thermocline perturbed by a semidiurnal internal wave.University of Texas Marine Science Institute Contribution No. 280.     

A linear programming model of residuals management for integrated iron and steel production

This paper briefly describes a linear programming model designed to allow the exploration of questions surrounding the management of the environmental impacts of integrated iron and steel facilities. In particular, the model can show how plant discharges will change in the absence of specific legal restrictions or effluent charges, with such variables as product mix, steel-furnace type, casting technology, and the scrap-ore price ratio. In addition, the costs implied by placing restrictions on discharges of specific residuals (e.g., BOD, oil, suspended solids, particulates) may be estimated, or response to proposed effluent charges may be predicted.     

Using global sensitivity analysis of demographic models for ecological impact assessment

Population viability analysis (PVA) is widely used to assess population‐level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input‐parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input‐parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea‐level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions.     

Evaluating long-term hydrological impacts of regional urbanisation in Hanyang,China, using a GIS model and remote sensing

China has experienced a rapid urbanisation, especially since the 1980s; however, the environmental impacts of this process are not fully investigated. Hanyang (Hubei Province, south China) was selected as a typical case to investigate runoff and non-point source (NPS) pollution impacts of urbanisation. A water quality simulation model (L-THIA) was applied to determine the long-term implications of different degrees of regional urbanisation impacts on NPS pollutants. Land-use patterns in 1987, 1998 and 2003 were analysed to evaluate the temporal variation of urbanisation, and the precipitation dataset from 1975 to 2003 was used to estimate the mean annual runoff and NPS pollutants. The contributions of different land-use categories to average annual runoff and NPS pollutant production were assessed by the means of a regression model. Results show that urban/impervious lands increased by 30.4% between 1987 and 2003, with the most increase occurring in 1998–2003. Industrial and forestlands have the most and least impact, respectively, on mean annual runoff and NPS pollutants. A combination of L-THIA and the regression model was found to be useful as a decision support tool for regional and urban planning from the perspectives of water quality control.     

European Union agricultural policy scenarios' impacts on social sustainability of agricultural holdings

This paper aims to assess the impacts of household behaviour on social sustainability by simulating agricultural policy scenarios of the European Union (EU) Common Agricultural Policy (CAP). For this purpose a multicriteria model was formulated, at farm household level, in order to study the social impacts of the CAPs using different scenarios. The scenarios were chosen on the basis of the main EU policies affecting farm households, such as the alternative crops scenario, the Water Framework Directive scenario and the agrienvironmental schemes scenario. The data for this analysis resulted from the CAP-IRE project, a European FP7-funded project. The model includes a utility function with several conflicting criteria such as maximization of gross margin and risk and labour minimization. The model is further used to simulate the impacts on social sustainability by estimating main social indicators. The model is applied in two different farm types in Greece. The results show that the CAP scenarios have multiple social impacts on agricultural holdings, and particularly on the farm labour structure. These impacts have negative effect on social sustainability.     

Optimal fishery management in the presence of illegal activity

A simple fishery model is developed with legal and illegal markets for fish, the latter market being combated by enforcement efforts put forth by a social regulator. In response to enforcement, violators undertake avoidance activities to escape detection. The possible impacts of illegal activity on optimal fishery management are then explored, and some policy implications are suggested. Concurrently, optimal regulation is calculated when: (a) only legal surplus is maximized versus (b) when both legal and illegal surplus is maximized. The rationale for these two regimes and their divergence in optimal management policies is outlined.     

Mathematical model and computer simulation of the population dynamics of zooplankton in lake and estuary ecosystems

A mathematical model is developed for the population dynamics and survival probabilities of zooplankton in lake and estuarine. Basic digital computer simulation methodology is used for systematic application of the mathematical model for analysis of the population dynamics of zooplankton. In the analysis of this theoretical modelling, attention is focused on the production and destruction processes of the animal. Both processes are assumed stationary. The birth (production) rate is constant and life-span distribution is independent of time. It is also assumed that the different zooplankton species are independent and share a common aquatic ecosystem, and what happens to one of them does not influence the fate of any other in this system.     

Secondary materials and international trade: A comment on the domestic market

Concentrating on wastepaper, Grace et al. [J. Environ. Econ. Manag. 5, 172–186 (1978)] have argued that international trade in secondary materials can effectively help to stabilize their prices. In their modeling, however, they erroneously assume that pulp sets the upper bound for the price of wastepaper. This paper presents evidence to the contrary. Pulp contracts are typically long term, so wastepaper is frequently an input of last resort. The wastepaper price can therefore easily exceed the price pulp when output demand is high. A geometric model is presented to formalize this explanation, and it is argued from there that the increased stability offered by trade is more significant than suggested by Grace et al.     

Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

Exploring price effects on the residential water conservation technology diffusion process: a case study of Tianjin city

Reforms of the water pricing management system and the establishment of a flexible water pricing system are significant for cities in northern China to tackle their critical water issues. The WATAP (Water conservation Technology Adoption Processes) model is developed in order to capture the water conservation technology adoption process under different price scenarios with disaggregate water demands down to the end use level. This model is explicitly characterized by the technological selection process under maximum marginal benefit assumption by different categories of households. In particular, when households need to purchase water devices in the provision market with the consideration of complex factors such as the life span, investment and operating costs of the device, as well as the regulated water price by the government. Applied to Tianjin city, four scenarios of water price evolutions for a long-term perspective (from year 2011 to 2030) are considered, including BAU (Business As Usual), SP1 (Scenario of Price increase with constant annual rate), SP2 (Scenario of Price increase every four years) and SP3 (Scenario of Price increase with affordable constraint), considering many factors such as historic trends, affordability and incentives for conservation. Results show that on aggregate 2.3%, 11.0% and 18.2% of fresh water can be saved in the residential sector in scenario SP1, SP2 and SP3, respectively, compared with the BAU scenario in the year 2030. The water price signals can change the market shares of different water appliances, as well as the water end use structure of households, and ultimately improve water use efficiency. TheWATAP model may potentially be a helpful tool to provide insights for policy makers on water conservation technology policy analysis and assessment.     

Estimating the footprint of pollution on coral reefs with models of species turnover

Ecological communities typically change along gradients of human impact, although it is difficult to estimate the footprint of impacts for diffuse threats such as pollution. We developed a joint model (i.e., one that includes multiple species and their interactions with each other and environmental covariates) of benthic habitats on lagoonal coral reefs and used it to infer change in benthic composition along a gradient of distance from logging operations. The model estimated both changes in abundances of benthic groups and their compositional turnover, a type of beta diversity. We used the model to predict the footprint of turbidity impacts from past and recent logging. Benthic communities far from logging were dominated by branching corals, whereas communities close to logging had higher cover of dead coral, massive corals, and soft sediment. Recent impacts were predicted to be small relative to the extensive impacts of past logging because recent logging has occurred far from lagoonal reefs. Our model can be used more generally to estimate the footprint of human impacts on ecosystems and evaluate the benefits of conservation actions for ecosystems.     

Non-renewable resource prices: Deterministic or stochastic trends?

In this paper, we examine temporal properties of 11 natural resource real price series from 1870 to 1990. Recent studies by Ahrens and Sharma [Trends in natural resource commodity prices: deterministic or stochastic? J. Environ. Econom. Manage. 33(1997)59–74], Berck and Roberts [Natural resource prices: will they ever turn up? J. Environ. Econom. Manage. 31(1996)65–78], and Slade [Grade selection under uncertainty: least cost last and other anomalies, J. Environ. Econom. Manage. 15(1988)189–205], among others, find that many non-renewable resource prices have a stochastic trend. We revisit this issue by employing a Lagrangian multiplier unit root test that allows for two endogenously determined structural breaks with and without a quadratic trend. Contrary to previous research, we find evidence against the unit root hypothesis for all price series. Our findings support characterizing natural resource prices as stationary around deterministic trends with structural breaks. We additionally show that both pre-testing for unit roots with breaks and allowing for breaks in the forecast model can improve forecast accuracy. Overall, the results in this paper are important in both a positive and normative sense; without an appropriate understanding of the dynamics of a time series, empirical verification of theories, forecasting, and proper inference are potentially fruitless.     

Stochastic Trends and Structural Breaks in the Intensity of Metals Use

This research examines whether innovations in the relationship between metals demand and economic activity are of a deep nature, such that they have persistence, or whether such innovations are transitory, conditional on a break point resulting from the 1973 oil price shock. Evidence was found to suggest that, at least for some of the metals studied in this research, intensity of use is well represented as a stationary process after accounting for a break point at 1973. One interpretation of this result is that technological shocks are not as pervasive as one might have thought and that only extraordinary events have a measurably long-lasting influence on metals demand.     

A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter

A method for quantifying source impacts for secondary PM_2.5 species is derived. The method provides estimates of bias in modeled concentrations. Adjusted concentrations match corresponding observations at monitored locations. Sources impacts on secondary species are estimated over the US for 20 sources. Community Multi-Scale Air Quality (CMAQ) estimates of sulfates, nitrates, ammonium, and organic carbon are highly influenced by uncertainties in modeled secondary formation processes, such as chemical mechanisms, volatilization, and condensation rates. These compounds constitute the majority of PM_2.5 mass, and reducing bias in estimated concentrations has benefits for policy measures and epidemiological studies. In this work, a method for adjusting source impacts on secondary species is developed that provides estimates of source contributions and reduces bias in modeled concentrations compared to observations. The bias correction adjusts concentrations and source impacts based on the difference between modeled concentrations and observations while taking into account uncertainties at the location of interest; and it is applied both spatially and temporally. We apply the method over the US for 2006. The mean bias for initial CMAQ concentrations compared to observations is −0.28 (OC), 0.11 (NO₃), 0.05 (NH₄), and −0.08 (SO₄). The normalized mean bias in modeled concentrations compared to observations was effectively zero for OC, NO₃, NH₄, and SO₄ after applying the secondary bias correction. 10-fold cross-validation was conducted to determine the performance of the spatial application of the bias correction. Cross-validation performance was favorable; correlation coefficients were greater than 0.69 for all species when comparing observations and concentrations based on kriged correction factors. The methods presented here address model uncertainties by improving simulated concentrations and source impacts of secondary particulate matter through data assimilation. Secondary-adjusted concentrations and source impacts from 20 emissions sources are generated for 2006 over continental US.     

Interactive effects of natural and human disturbances on vegetation dynamics across landscapes.

Accurate measures of human effects on landscape processes require consideration of both the direct impacts from human activities and the indirect consequences of the interactions between humans and the landscape. This is particularly evident in systems experiencing regular natural disturbances such as in the mountainous areas of southwestern China, where the remaining population of giant pandas (Ailuropoda melanoleuca) is supported. Here the spatiotemporal patterns of human impacts, forests, and bamboo episodic die-offs combine to determine the distribution of panda habitat. To study the complex interactions of humans and landscapes, we developed an integrated spatiotemporally explicit model of household activities, natural vegetation dynamics, and their impacts on panda habitat. Using this model we examined the direct consequences of local fuelwood collection and household creation on areas of critical giant panda habitat and the indirect impacts when coupled with vegetation dynamics. Through simulations, we found that over the next 30 years household impacts would result in the loss of up to 30% of the habitat relied on by pandas during past bamboo die-offs. The accumulation and spatial distribution of household impacts would also have a considerable indirect influence on the spatial distribution of understory bamboo. While human impacts influence both bamboo die-off and regeneration, over 19% of pre-existing low-elevation bamboo habitat may be lost following an episodic die-off depending on the severity of the impacts and timing of the die-offs. Our study showed not only the importance of the spatial distribution of direct household impacts on habitat, but also the far-reaching effects of the indirect interactions between humans and the landscapes they are modifying.     

Model parameterization for aerobic decomposition of plant resources drowned during man-made lakes formation

The formation of man-made reservoirs generates several impacts on water quality. In order to minimize some of these impacts mathematical models are currently used. This paper aims to discuss the issue associated with the degradation of plant resources (leaves, branches, barks and litter) that remain within the watershed of the new man-made lakes and parameterize a kinetic model related to decay of plant detritus. In these environments, the short-term variation of limnological parameters is mainly connected with biomass decay drowned during the filling operation. The kinetics of the degradation processes in reservoirs are discussed on the basis of information with related to detritus sources and the chemical properties of different types of compounds (i.e. labile and refractory fractions). Overall, the parameterization of the (first order) kinetic model showed that refractory fractions (ca. 86%) are predominant and the mineralization is a slow process, constituting the main route for decomposition and being affected by changes of environmental variables. The mineralization of labile and hydrosoluble compounds (ca. 14%) is responsible for the short-term water quality variation owing to decomposition; basically, the intensities of these changes depend on the labile/soluble compounds content of detritus and its chemical composition.     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.     

An environmental assessment of enhanced oil recovery

The objective of this assessment is to quantify some of the environmental effects of a significant increase in United States oil production by tertiary or enhanced oil recovery (EOR) methods. The problems associated with each EOR technology are discussed and controls and regulations are briefly summarised. A tertiary oil production scenario for the United States was developed focussing only on mainland fields in the lower 48 states. It included all of the EOR methods expected to be in use during the next two decades. The environmental impacts, including water requirements, air emissions and generation of solid wastes, are then scaled to this scenario. The effects of control technologies and state regulations are considered. A comparison is also made between the impacts of EOR as an energy source and impacts associated with coal and synthetic fuels which concludes that EOR is preferable in many respects. There are environmental risks associated with EOR technologies, specific projects and specific fields in the production scenario; however, most problems are solvable by responsible regulation, enforcement of regulations and application of the best professional engineering by project operators.     

The higher price of cleaner fuels: Market power in the rail transport of fuel ethanol

This paper provides evidence of market power in the transportation of ethanol used in reformulated gasoline and alternative transportation fuels. I estimate a reduced form model for railroad route-level prices. My identification strategy instruments for railroad entry, controls for selection and explicitly models capacity constraints. A detailed understanding of this industry is important because U.S. environmental policies seek to substantially expand ethanol use. Evidence of market power may alter the types of policies pursued by lawmakers. I find that ethanol shipment prices are lower for more competitive routes. I also find evidence that railroads price discriminate based on environmental regulation at route destinations. Monopolist prices for shipments to carbon monoxide non-attainment areas are 3% higher than shipments to other destinations. This price premium falls sharply with increased competition. This suggests a perverse result where environmental regulation increases the price of a clean input.