Dynamic responses and tail-dependence among commodities,the US real interest rate and the dollar

Theoretical models suggest monetary policy is transmitted to commodity prices. We quantify this channel using several empirical methods under daily data. In early 2009, the US real interest rate became negative, with sample mean varying from 1.75 % (in the mid-1997 to January 28, 2009, subsample) to \(-1.50\,\%\) (in January 29, 2009, to mid-September 2013 subsample). Gold displays higher risk-adjusted returns earlier, while copper and oil have higher risk-adjusted returns more recently. Shocks to the exchange rate and the real interest rate in VARs explain almost 30 % for oil and 32 % for copper more recently when impulse responses are more significant. The time-varying correlation of oil with the real interest rate in the more recent period is \(-0.462\), and its correlation with the exchange rate is \(-0.460\), compared to \(-0.089\) and \(-0.120\), respectively, in the earlier period. Vine copula methods identify a dependence pattern of C-vine copula with t-copula in almost every pair among commodity prices, the real value of the US dollar and the US real interest rate.     

The Impact of Climate Change on Regional Government Expenditures: Evidence from Russia

This paper explores an almost untouched topic in the fast-growing climate econometrics literature—the implications of climate change for government expenditures. Using a rich sub-national dataset for Russia covering 1995–2009, we estimate the impacts of changes in climatic conditions through short-term variation and medium-term changes in average regional temperatures and precipitation. We show a strong and robust negative (but non-linear) relation between regional budget expenditures and population-weighted temperature. The results indicate that an increase in temperature results in a decrease in public expenditures and that the magnitude of this effect diminishes the warmer the region. Further, our results suggest that the benefits from warming accumulate and that adaptation measures could help leverage those benefits. The estimated decreases in regional government expenditure are, however, quite small. In mild warming scenarios, according to our estimates Russia saves roughly USD 3–4 billion in regional government expenditures between 2000 and the 2020s without undertaking adaptation measures, depending on the scenario. It should be noted that our results are estimated for a scenario of mild temperature increase (1–2 \(^{\circ }\)C). Larger temperature increases are likely to have dramatic consequences e.g. from loss of permafrost and methane release that are impossible to predict with available historical data.     

Technical Synergies and Trade-Offs Between Abatement of Global and Local Air Pollution

In this paper, we explore the synergies and tradeoffs between abatement of global and local pollution. We build a unique dataset of Swedish combined heat and power plants with detailed boiler-level data 2001–2009 on not only production and inputs but also on emissions of \(\hbox {CO}_{2}\) and \(\hbox {NO}_{\mathrm{x}}\). Both pollutants are regulated by strict policies in Sweden. \(\hbox {CO}_{2}\) is subject to the European Union Emission Trading Scheme and Swedish carbon taxes; \(\hbox {NO}_{\mathrm{x}}\)—as a precursor of acid rain and eutrophication—is regulated by a heavy fee. Using a quadratic directional output distance function, we characterize changes in technical efficiency as well as patterns of substitutability in response to the policies mentioned. The fact that generating units face a trade-off between the pollutants indicates the need for policy coordination.     

The Value of Air Quality in Chinese Cities: Evidence from Labor and Property Market Outcomes

Using a dual-market sorting model of workers’ location decisions, this paper studies the capitalization of air pollution in wages and property prices across Chinese cities. To account for endogeneity of air pollution in the determination of wages and property prices, we exploit quasi-experimental variation in air quality induced by a policy subsidizing coal-based winter heating in northern China, and document a discontinuity in average air quality for cities located north and south of the policy boundary. Using data for all 288 Chinese cities in 2011, we estimate an equilibrium relationship between wages and house prices for the entire system of Chinese cities, and specify a regression discontinuity design to quantify how variation in air quality induced by the policy affects this relationship locally. Our preferred estimates of the elasticity of wages and house prices with respect to \(\text {PM}_{10}\) concentration are 0.53 and \({-}\) 0.71 respectively. At the average of our sample, the willingness to pay for a unit reduction in \(\text {PM}_{10}\) concentration is CNY 261.28 (\(\simeq \) USD 40.50), with a significant share reflected in labor market outcomes.     

Controlling Episodic Air Pollution with a Seasonal Gas Tax: The Case of Cache Valley,Utah

Using daily data spanning 10 years, we establish a statistical relationship between episodic particulate-matter \((\hbox {PM}_{2.5})\) concentrations and vehicle trips in Cache Valley, Utah, and estimate an average gas-price elasticity for the region. We also estimate the benefits and costs associated with a seasonal gas tax set to reduce vehicle trips during the winter-inversion season and thereby lower health costs through concomitant decreases in the \(\hbox {PM}_{2.5}\) concentrations. We find a strong positive relationship between vehicle trips reduced and associated reductions in \(\hbox {PM}_{2.5}\) concentrations. Further, we estimate a mean gas price elasticity of approximately \(-\)0.3 in what we call a “high price variability environment.” Incorporating these results, cost-benefit analysis suggests that the social net benefit for Cache Valley associated with the imposition of a seasonal gas tax during the winter-inversion season is highly dependent upon the type of benefit estimation method used.     

Further results on essential Nash equilibria in normal-form games

A Nash equilibrium \(x\) of a normal-form game \(G\) is essential if any perturbation of \(G\) has an equilibrium close to \(x\). Using payoff perturbations, we identify a new collection of games containing a dense, residual subset of games whose Nash equilibria are all essential. This collection covers economic examples that cannot be handled by extant results and subsumes the sets of games considered in the literature.     

Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4 \(^{\circ }\)C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature–damage relationship, we find a three- to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four- to five-fold.     

Fiscal Policy and $$\hbox {CO}_{2}$$ Emissions of New Passenger Cars in the EU

To what extent have national fiscal policies contributed to the decarbonisation of newly sold passenger cars? We construct a simple model that generates predictions regarding the effect of fiscal policies on average \(\hbox {CO}_{2}\) emissions of new cars, and then test the model empirically. Our empirical strategy combines a diverse series of data. First, we use a large database of vehicle-specific taxes in 15 EU countries over 2001–2010 to construct a measure for the vehicle registration and annual road tax levels, and separately, for the \(\hbox {CO}_{2}\) sensitivity of these taxes. We find that for many countries the fiscal policies have become more sensitive to \(\hbox {CO}_{2}\) emissions of new cars. We then use these constructed measures to estimate the effect of fiscal policies on the \(\hbox {CO}_{2}\) emissions of the new car fleet. The increased \(\hbox {CO}_{2}\)-sensitivity of registration taxes have reduced the \(\hbox {CO}_{2}\) emission intensity of the average new car by 1.3 %, partly through an induced increase of the share of diesel-fuelled cars by 6.5 percentage points. Higher fuel taxes lead to the purchase of more fuel efficient cars, but higher diesel fuel taxes also decrease the share of (more fuel efficient) diesel cars; higher annual road taxes have no or an adverse effect.     

A trend filtering method closely related to $$\ell _{1}$$ trend filtering

The filtering method developed by Kim et al. (SIAM Rev 51:339–360, 2009), \(\ell _{1}\) trend filtering, is attractive because it enables us to estimate a continuous piecewise linear trend. This paper introduces a new filtering method closely related to \(\ell _{1}\) trend filtering in order to contribute to the accumulation of knowledge on \(\ell _{1}\) trend filtering. We show that the piecewise linearity, which is the key feature of \(\ell _{1}\) trend filtering, is derived from the new filtering. For this reason, we refer to the filtering as ‘pure’ \(\ell _{1}\) trend filtering. We also demonstrate some other miscellaneous results concerning the new filtering.     

Shaping the Relationship Between Economic Development and Carbon Dioxide Emissions at the Local Level: Evidence from Spatial Econometric Models

The debate over the correlation between economic growth and environmental pollution has attracted a great deal of attention from academic researchers and policy makers in recent years. There has been excessive use of spatial econometric models and too much emphasis on statistical procedures in empirical studies. In this study, we contribute to the existing literature by conducting a more rigorous analysis of the relationship between economic growth and \(\hbox {CO}_{2}\) emissions in Chinese cities using spatial Durbin models. Our results show that \(\hbox {CO}_{2}\) emissions increase monotonically in relation to economic growth at the city level and that the driving effects of economic growth are slightly smaller in central China than in eastern and western China. In addition to economic growth, industry’s share of the economy is a major driver of \(\hbox {CO}_{2}\) emissions, while technological improvement, measured by energy intensity per unit of gross domestic product (GDP), and the effectiveness of environmental governance flatten the shape of the environmental Kuznets curve. We provide evidence of local spillover effects of explanatory variables on \(\hbox {CO}_{2}\) emissions. Economic competition as well as technological diffusion are found to exist in Chinese cities in relation to \(\hbox {CO}_{2}\) emissions. We also find carbon leakage between cities only if the per capita GDP of a given city is less than $493 (in 2010 constant dollars). Results hold when robustness checks are performed. Policy makers should carefully consider regional differences and the inherent spatial interactions between factors when formulating carbon reduction policies.     

Is the Green Solow Model Valid for $$\hbox {CO}_{2}$$ Emissions in the European Union?

This paper addresses the \(\hbox {CO}_{2}\) emission patterns of the European Union from 1950 to 2010, and examines the validity of the Green Solow model, which simulates \(\hbox {CO}_{2}\) emissions growth by including only Solow forces and assuming emission intensity growth to be exogenous and constant. This study verifies that an environmental Kuznets curve (EKC) trajectory exists for per capita \(\hbox {CO}_{2}\) emissions in the European Union, that emission intensity growth is decreasing over time, and that the decreasing intensity growth reflects variations of the dependent variable in the specifications of the Green Solow model. The critique by Stefanski (On the mechanics of the Green Solow model. OxCarre Research Paper 47, OxCarre & Laval University, Oxford, 2013) of the Green Solow model assumption of exogenous and constant intensity growth is validated. The EKC is defined as the emissions plotted against income and emission intensity is defined as the ratio of emissions to income. The EKC and emission intensity share identical definitions and similar transition trajectories over time. The transition of the EKC trajectory and decline in emission intensity growth began before worldwide attention was focused on global warming.     

Tops-only domains

In this paper we consider the standard voting model with a finite set of alternatives A and n voters and address the following question: what are the characteristics of domains \({\mathcal D}\) that induce the property that every strategy-proof social choice function \({f: {\mathcal D}^n \rightarrow A}\) satisfying unanimity, has the tops-only property? We first impose a minimal richness condition which ensures that for every alternative a, there exists an admissible ordering where a is maximal. We identify conditions on \({\mathcal D}\) that are sufficient for strategy-proofness and unanimity to imply tops onlyness in the general case of n voters and in the special case, n = 2. We provide an algorithm for constructing tops-only domains from connected graphs with elements of A as nodes. We provide several applications of our results. Finally, we relax the minimal richness assumption and partially extend our results.     

Offsetting Versus Mitigation Activities to Reduce $$\hbox {CO}_{2}$$ Emissions: A Theoretical and Empirical Analysis for the U.S. and Germany

This paper studies the voluntary provision of public goods that is partially driven by a desire to offset for individual polluting activities. We first extend existing theory and show that offsets allow a reduction in effective environmental pollution levels while not necessarily extending the consumption of a polluting good. We further discuss the impact of an increased environmental preference on purchases of offsets and mitigation activities. Several theoretical results are then econometrically tested using a novel dataset on activities to reduce \(\hbox {CO}_{2}\) emissions for the case of vehicle purchases in the U.S. and Germany. We show that environmental preference triggers the stated use of \(\hbox {CO}_{2}\) offsetting and mitigation channels in both countries. However, we find strong country differences for the stated purchase of \(\hbox {CO}_{2}\) offsets. While such activities are mainly triggered by a high general awareness of the climate change problem in the U.S., the perception that road travel is responsible for \(\hbox {CO}_{2}\) emissions to a large extent is more important for driver’s license holders in Germany.     

Fractional Integration Versus Structural Change: Testing the Convergence of $$\hbox {CO}_{2}$$ Emissions

This paper assesses the stochastic convergence of relative \(\hbox {CO}_{2}\) emissions within 28 OECD countries over the period 1950–2013. Using the local Whittle estimator and some of its variants we assess whether relative per capita \(\hbox {CO}_{2}\) emissions are long memory processes which, although highly persistent, may revert to their mean/trend in the long run thereby indicating evidence of stochastic convergence. Furthermore, we test whether (possibly) slow convergence or the complete lack of it may be the result of structural changes to the deterministics of each of the relative per-capita emissions series by means of the tests of Berkes et al. (Ann Stat 1140–1165, 2006) and Mayoral (Oxford Bull Econ Stat 74(2):278–305, 2012). Our results show relatively weak support for stochastic convergence of \(\hbox {CO}_2\) emissions, indicating that only between 30 and 40% of the countries converge to the OECD average in a stochastic sense. This weak evidence disappears if we enlarge the sample to include 4 out of the 5 BRICS, indicating that our results are not robust to the inclusion of countries which are experiencing rates of growth which are far larger than those of the OECD members. Our results also decisively indicate that a slow or lack of convergence is not the results of a structural break in the relative \(\hbox {CO}_{2}\) emissions series.     

Shadow prices of $$\hbox {CO}_{2}$$ emissions at US electric utilities: a random-coefficient,random-directional-vector directional output distance function approach

We estimate the shadow prices of \(\hbox {CO}_{2}\) emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of \(\hbox {CO}_{2}\) emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of \(\hbox {CO}_{2}\) emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.     

How Much Does Wind Power Reduce $$\text {CO}_{2}$$ Emissions? Evidence from the Irish Single Electricity Market

This paper evaluates the effect of wind generation on \(\text {CO}_{2}\) emissions using 2008–2012 historical data for the Irish Single Electricity Market. Wind generation displaces \(\text {CO}_{2}\) emissions, as expected, in line with the average system emissions. Over the whole period, wind generation avoided about 8.8 million tons of \(\text {CO}_{2}\), equivalent to about 12% of total system emissions. To understand what drives the level of abatement we evaluate the results by technology and determine that wind generation has similar effects on total emissions from CCGT and coal plants, due to the higher carbon content of coal. Each MWh of wind, however, replaces more generation from CCGTs than from coal plants, in proportion to their generation. We also test the hypothesis that as wind displaces baseload plants it pushes them to generate less efficiently, but find no evidence of a strong negative effect of wind on CCGT or coal plant efficiency. Finally wind displaces about 2.5% fewer emissions when the pumped storage plant is on outage, suggesting that wind is more effective when paired with a flexible system.     

Lossed in translation: an off-the-shelf method to recover probabilistic beliefs from loss-averse agents

Strictly proper scoring rules are designed to truthfully elicit subjective probabilistic beliefs from risk neutral agents. Previous experimental studies have identified two problems with this method: (i) risk aversion causes agents to bias their reports toward the probability of \(1/2\), and (ii) for moderate beliefs agents simply report \(1/2\). Applying a prospect theory model of risk preferences, we show that loss aversion can explain both of these behavioral phenomena. Using the insights of this model, we develop a simple off-the-shelf probability assessment mechanism that encourages loss-averse agents to report true beliefs. In an experiment, we demonstrate the effectiveness of this modification in both eliminating uninformative reports and eliciting true probabilistic beliefs.     

Spatial econometric Monte Carlo studies: raising the bar

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification. A major point is that practitioners rely on scalar summary measures of direct and indirect effects estimates to interpret the impact of changes in explanatory variables on the dependent variable of interest. We argue that these should be the focus of Monte Carlo experiments. Since effects estimates reflect a nonlinear function of both \(\beta \) and \(\rho \), past studies’ focus exclusively on \(\beta \) and \(\rho \) parameter estimates may not provide useful information regarding statistical properties of effects estimates produced by alternative estimators. Since effects estimates have recently become the focus of inference regarding the significance of (scalar summary) direct and indirect impacts arising from changes in the explanatory variables, empirical measures of dispersion produced by simulating draws from the (estimated) variance–covariance matrix of the parameters \(\beta \) and \(\rho \) should be part of the Monte Carlo study. An implication is that differences in the quality of estimated variance–covariance matrices arising from alternative estimators also plays a role in determining the accuracy of inference. An applied illustration is used to demonstrate how these issues can impact conclusions regarding the performance of alternative estimators.     

Quantifying knowledge exchange in R&D networks: a data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets, extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. These data are used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in eight dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate μ for an alliance duration τ. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). This is a new measure that takes in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm’s position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.     

Corporate pollution control strategies and labor demand: evidence from China’s manufacturing sector

The loss of employment is an essential issue that looms large in policy debates on pollution control. This paper estimates the impact of pollution reduction on labor demand in China’s manufacturing sector in the period 2001–2007. We conduct this research by using a sample with unbalanced panel data matched from two unique datasets of environmental statistics and an industrial economic database. Using the environmental performance of peer firms as the instrumental variable, our overall results show that improvements in environmental performance through reductions in \( SO _2\) emissions and COD emissions led to a statistically significant reduction in employment. On average, a 1% reduction in \( SO _2\) (COD) emissions causes a reduction in labor demand of approximately 0.018–0.019% (0.012–0.013%). We complement existing studies by carefully examining the impacts of firms’ different abatement strategies on labor demand. We find that pollution reduction through pollution prevention has substitutive effects on employment and that pollution reduction through pollution control at the end of the production process may require additional workers and thus has positive but not significant effects on labor demand. Finally, pollution control has heterogeneous effects on labor demand by different types of polluting firms (e.g., ownership, region, and industry).