Explaining the secular stagnation of productivity growth is a widely recognized challenge to economists and policymakers. One potentially important explanation without much attention concerns the ongoing low-carbon transition. This paper explores whether considering greenhouse gas emissions can explain productivity stagnation in OECD countries. We propose a quantile shadow-price Fisher index to gauge green total factor productivity (TFP) based on the newly developed penalized convex quantile regression approach. The quantile shadow-price Fisher index requires neither the real price data nor an ad hoc choice of quantiles and allows the quantiles to move in the inter-period sample. An empirical application to 38 OECD countries during 1990--2019 demonstrates that the measured productivity growth is considerably higher when the GHG emissions are accounted for. For countries that have reduced GHG emissions most actively, the average green TFP growth rate could double the conventional TFP growth. The impacts of ignoring human capital and different representations of fixed capital on green TFP growth are also discussed explicitly.
Undesirable outputs (or bads) refer to the byproducts accompanied with desirable outputs (or goods) in a production process, e.g. sulfur dioxide and carbon dioxide in coal-fired power generation. The shadow price of undesirable output, which may be interpreted as the opportunity cost of abating one additional unit of undesirable output in terms of the loss of desirable output, could provide valuable reference information for policy analysis and making. A prevalent practice is to use the Shephard or directional distance function to derive the shadow price, which can be further calculated by parametric or nonparametric efficiency models. In application, earlier studies have estimated shadow prices at plant, sector and even economy levels. This study aims to conduct a systematic review of the studies on estimating shadow prices of undesirable outputs with efficiency models. We first introduce the methodological framework for deriving shadow prices as well as the nonparametric/parametric efficiency models for calculating their values. A systematic summary of over forty earlier studies in this field is then conducted, through which the key features of the existing studies are summarized and possible future research directions are identified.
Shanghai, one of the most developed cities in China, is implementing a pilot regional carbon emission trading scheme. Estimating the marginal abatement costs of CO2 emissions for the industrial sectors covered in Shanghai's emission trading scheme provides the government and participating firms useful information for devising compliance policies. This paper employs multiple distance function approaches to estimating the shadow prices of CO2 emissions for Shanghai industrial sectors. Our empirical results show that the overall weighted average of shadow price estimates by different approaches ranges between 394.5 and 1906.1Yuan/ton, which indicates that model choice truly has a significant effect on the shadow price estimation. We have also identified a negative relationship between the shadow price of CO2 emissions and carbon intensity, and the heavy industries with higher carbon intensities tend to have lower shadow prices. It has been suggested that Shanghai municipal government take various measures to improve its carbon market, e.g. using the marginal abatement costs of participating sectors/firms as a criterion in the initial allocation of carbon emission allowances.
Past decade has seen numerous data envelopment analysis (DEA)-based energy efficiency studies, which usually treat the production process as a "black box" and ignore the internal production information. This paper takes into account the joint inputs and sub joint inputs to reveal the specific information on how inputs are allocated to outputs. To this end, we first propose an extended output-specific production technology based on which two novel energy efficiency measures are developed. We also present an empirical study on 32 countries to demonstrate the novelty and the usefulness of our method. We find that our output-specific energy efficiency measures provide a more straightforward way of dealing with undesirable output and are better capable of identifying inefficient production behavior.
A large number of studies on environmental productivity have appeared across various sub-disciplines of economics as well as in other related disciplines such as operations research and engineering. In these studies, the production units of interest are usually plants or firms, sectors or industries, regions, and countries. To our knowledge, however, only one previous study considers environmental performance of consumer durables. This is somewhat surprising because, during their use phase, consumer durables such as passenger cars and home appliances are in fact production units that consume energy and resources to provide services for consumers, and hence are also contributors to various environmental pollutants. This chapter aims to develop an environmental productivity index specially designed for consumer durables. To this end, we first analyze the particular features of consumer durables compared to conventional production units. Based on these features, we elaborate how to model the production activity during the use phase of consumer durables; and then we present an overview of the existing approaches to measuring environmental productivity change and describe how they can be applied in the current context. Finally, we use a unique Finnish data set of passenger cars to illustrate the interpretation of the proposed index.
High economic cost of climate policy has attracted critical debate since the Kyoto Protocol. However, reliable empirical evidence of the abatement cost of green-house gases across countries remains scant. In this study we estimate the average yearly green-house gas abatement costs per capita for a panel of 28 OECD countries in years 1990-2015. The marginal abatement costs are estimated using a novel data-driven approach based on convex quantile regression. Compared to traditional frontier estimation methods, the quantile approach takes into account a broader set of abatement options and is more robust to inefficiency, noise, and heteroscedasticity in empirical data. The comparison of OECD countries shows that the actual abatement cost per capita has been very modest, much lower than predicted in the late 1990s. This result has profound policy implications, calling for more ambitious climate change mitigation strategy in the future.
Evaluation of abatement costs is critical in setting reduction goals and devising climate policy. However, reliable forward-looking assessment of the short-term effects of climate policy remains a major challenge. Using panel data of 30 Chinese provinces during 1997-2015, we first estimate the marginal CO2 abatement costs using a novel data-driven approach, convex quantile regression. Based on the marginal abatement cost estimates and China's plans regarding carbon intensity reduction and economic growth, we present a forward-looking assessment of the abatement costs for Chinese provinces for 2016-2020. Our main finding is that all the Chinese provinces have a negative abatement cost, which means these provinces can benefit from an increase in the absolute level of CO2 emissions despite the constraint on carbon intensity. The magnitudes of economic benefits exhibit a significant regional disparity because some provinces can increase more CO2 emissions than others. However, there is still costly carbon intensity abatement relative to a counterfactual where the provinces meet their economic growth targets but in the absence of the intensity reduction constraints. Policy implications have been proposed to enhance the efficiency and fairness of climate policy in China.
Transition towards a low-carbon transport sector fundamentally depends on decarbonization of the passenger car fleet. Therefore, it is critically important to understand the driving factors behind decreasing CO2 emissions of new passenger cars. This paper develops a new decomposition method to break down the change in the average CO2 emissions of new passenger cars into components representing changes in available technology, carbon efficiency of consumer choices, vehicle attributes, fuel mix, and the gap between type-approval and on-road CO2 emissions of passenger cars. Our decomposition draws insights from the traditional index decomposition analysis and frontier-based decomposition of productivity growth. It satisfies such desirable properties as factor reversal, time reversal, and zero-value robustness. An empirical application to a unique data set that covers all registered passenger cars in Finland sheds light on why and how the CO2 emissions of new cars decreased from year 2002 to year 2014.
This paper proposes a novel data-driven approach to estimate the navigable capacity of busy waterways, focusing on ships entering and leaving port, based on the structural characteristics of traffic flow driven by the Automatic Identification System (AIS) data. First, we collect the ship traffic flow in a busy waterway by processing the original AIS data and then identify the structural characteristics of the traffic flow using the K-means clustering algorithm. The clusters are constructed based on the spatiotemporal consumption of waterway resources of different ships and the waste of waterway resources caused by navigational mode conversion, taking ship domain into consideration. We apply the proposed approach to estimate the navigable capacity of the Dagusha Channel of Tianjin Port, China. The empirical results reveal that the maximum daily traffic capacity of the Dagusha Channel is about 109 ship times/day. A comparison of waterway capacity estimation methods demonstrates that our proposed approach is more accurate and able to quantify the waterway capacity of different types of ships in a busy waterway, taking the structural characteristics of traffic flow explicitly into account. The proposed approach provides support for the design of channel and determination of scheduling schemes for ships in busy waterways.
Marginal abatement cost (MAC) is a critically important concept for efficient environmental policy and management. In this paper we argue that most empirical studies using frontier estimation methods such as data envelopment analysis (DEA) over-estimate MACs. The first methodological contribution of this paper is to clarify the conceptual distinction between the shadow price and MAC in order to analyze three sources of upward bias due to the limited set of abatement options, inefficiency, and noisy data. Our second methodological contribution is to develop a novel MAC estimation approach based on convex quantile regression. Compared to the traditional methods, convex quantile regression is more robust to the choice of the direction vector, random noise, and heteroscedasticity. Empirical application to the U.S. electric power plants demonstrates that the upward bias of DEA may be a serious problem in real-world applications.
The penetration of smartphones into human life finds expression in problematic smartphone use, particularly under the Covid-19 home confinement. Problematic smartphone use is accompanied by adverse impacts on personal wellbeing and individual performance. However, little is known about the mechanism of such adverse impacts. Motivated by this, the present study strives to answer (i) how bedtime smartphone use impacts students’ academic performance through wellbeing-related strains; (ii) how to mitigate the adverse consequences of bedtime smartphone use. Drawing upon the stressor-strain-outcome paradigm, the current work presents a comprehensive understanding of how smartphone use indirectly deteriorates college students’ academic performance through the mediators of nomophobia — “the fear of being unavailable to mobile phones” (Lin et al., 2021) — and sleep deprivation. This allows a more flexible remedy to alleviate the adverse consequences of smartphone use instead of simply limiting using smartphones. This study collects a two-year longitudinal dataset of 6093 college students and employs the structural equation modeling technique to examine the stressor‐strain‐outcome relationship among bedtime smartphone use, nomophobia, sleep deprivation, and academic performance. This study finds robust evidence that wellbeing-related strains (i.e., nomophobia and sleep deprivation) mediate the negative relationship between bedtime smartphone use and academic performance. Furthermore, engaging in physical activity effectively mitigates the adverse effects of bedtime smartphone use upon nomophobia and sleep deprivation. This study not only enriches the current literature regarding the indirect effect mechanism of smartphone use but also provides valuable insights for academics and educational policymakers.
Purpose - As the number of social media users continues to rise globally, a heated debate emerges on whether social media use improves or harms mental health, as well as the bidirectional relation between social media use and mental health. Motivated by this, the authors' study adopts the stressor-strain-outcome model and social compensation hypothesis to disentangle the effect mechanism between social media use and psychological well-being. The purpose of this paper is to address this issue.Design/methodology/approach - To empirically validate the proposed research model, a large-scale two-year longitudinal questionnaire survey on social media use was administered to a valid sample of 6,093 respondents recruited from a university in China. Structural equation modeling was employed for data analysis.Findings - A longitudinal analysis reveals that social media use positively (negatively) impacts psychological well-being through the mediator of nomophobia (perceived social support) in a short period. However, social media use triggers more psychological unease, as well as more life satisfaction from a longitudinal perspective.Originality/value - This study addresses the bidirectional relation between social media use and psychological unease. The current study also draws both theoretical and practical implications by unmasking the bright-dark duality of social media use on psychological well-being.
Convex quantile regression (CQR) is a fully nonparametric approach to estimating quantile functions, which has proved useful in many applications of productivity and efficiency analysis. Importantly, CQR satisfies the quantile property, which states that the observed data is split into proportions by the CQR frontier for any weight in the unit interval. Convex expectile regression (CER) is a closely related nonparametric approach, which has the following expectile property: the relative share of negative deviations is equal to the weight of negative deviations. The first contribution of this paper is to extend these quantile and expectile properties to the general set of shape constrained nonparametric functions. The second contribution is to relax the global concavity assumptions of the CQR and CER estimators, developing the isotonic nonparametric quantile and expectile estimators. Our third contribution is to compare the finite sample performance of the CQR and CER approaches in the controlled environment of Monte Carlo simulations.
The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush-Kuhn-Tucker approximations.
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A direct approach to address this problem is to impose non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem.