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Companies affected by California’s cap-and-trade legislation are allotted certain credits for production that can be used or sold and can purchase additional credits from the state, which become a revenue source to be used for activities that reduce carbon emissions. The purpose of this paper is to investigate who ultimately pays for this program, its effectiveness in reducing carbon emissions in accordance with established goals, and the related effectiveness to advance social, economic, and environmental equity.

The methodology used for this research is secondary data analysis, triangulating three sources: California’s Climate Change Investment Reports, 2019-2021; repositories maintained by the California High-Speed Rail Authority and the California Air Resources Board; and a review of the literature and websites from other professional sources which addressed, directly and indirectly, the topics and questions explored in the study.

Key findings include evidence of enhancing social and environmental equity but ineffectiveness in reducing carbon emissions in accordance with state goals. Furthermore, the program displays evidence of economic inequity as it demonstrates characteristics of regressive taxation and an inability of low-income persons to acquire electric vehicles due to high costs.

The research effort is unique in that no other academic efforts were located which attempt to examine the cap-and-trade program’s effectiveness in attaining its goals.

The purpose of this paper is to obtain new empirical evidence about the connections between equity trading activity and five possible liquidity determinants: market capitalisation, dividend yield, earnings yield, company growth and the distinction between recently listed firms as opposed to more established ones.

The authors use a sample of 172 stocks from four European markets and estimate models using the entire sample data and different sub-samples to check the relative importance of the above determinants. The authors also conduct a factor analysis to re-classify the variables into a more succinct framework.

The evidence suggests that market capitalisation is the most important trading activity determinant, and the number of years listed ranks thereafter.

The positive relation between trading activity and market capitalisation is in line with prior literature, while the findings relating to the other determinants offer further empirical evidence which is a worthy addition in view of the contradictory results in prior research.

This study is of relevance to practitioners who would like to understand the cross-sectional variation in stock liquidity at a more detailed level.

The originality of the paper rests on two important grounds: the authors focus on trading turnover rather than on other liquidity proxies, since the former is accepted as an important determinant of the liquidity-generation process, and the authors adopt a rigorous approach towards checking the robustness of the results by considering various sub-sample configurations.

The United States devotes a larger share of its GDP to health care and spends more on health care per capita than any other country. The sheer size of the total spending on health care, at approximately $3.5 trillion in 2017, puts significant pressure on all payers and crowds out other forms of public and private spending.

In this brief commentary the authors suggest that, as part of the effort to deal with this pressure, the United States should look at borrowing a cost containment strategy from other countries: the use of hard caps on spending growth. The authors draw on our their experience of working with decision-makers over the last 20 years on the topic of priority setting to put forward some ideas on whether there is potential for application of trade-offs in the United States.

As hard caps force choices to be made, a necessary condition for successful implementation of this policy is the presence of an effective priority-setting framework to ensure that the right choices are made in operationalizing spending limitations. Work on this topic elsewhere can provide some insight into the use of a criteria-based framework for priority setting that purports transparency in decision-making to achieve value-based decisions.

Other countries still have much work to do, but there is a substantial track record of using formal priority-setting approaches that could potentially inform practice in the United States. We suggest that there are key segments of the US healthcare system where the adoption of formal priority-setting frameworks to guide trade-off decisions is feasible. Piloting such activity in these contexts is the next natural step in this line of inquiry.

This paper aims to review the literature on carbon trading from the perspective of evolution, finds out the evolution path of these literatures and gives out the future research hotspots in this field.

Uses visualization tools (CiteSpace and HistCite) to systematically categorize the literature on carbon-trading schemes in the Web of Science core collection from 1998 to 2018, comprehensively analyzes carbon-trading schemes from four dimensions, namely, discipline evolution, keyword evolution, citation cluster evolution and citation path evolution.

Research on carbon-trading schemes has a specific development and evolution path along four dimensions, namely, in the discipline dimension, the largest change lies in the mathematics pointed to by at least four different disciplines; the keyword evolution dimension shows a gradual deepening emphasis on coordinated development; citation clusters identify three major clusters – carbon prices, China’s carbon trading, carbon market and supply chain; and citation paths identify three major evolutionary paths, the most important of which shows that “What affects carbon price?” has changed to “What is the impact of carbon prices?”

Reveals the evolution path of carbon trading research studies and proposes four possible development directions for carbon-trading scheme research, which is helpful for future carbon trading-related research and serves as a reference for the promotion of and improvements in carbon-trading schemes.

The carbon emissions due to industrial production and market consumption activities are the major contributors to global warming. With the signing of UN Paris Accord 2016 on climate change, the world's major countries are devising measures to combat climate change and attain a sustainable, low-carbon future. Globalization demands companies not only to adopt greener manufacturing practices internally for reduced carbon footprint (CFP) but beyond its boundaries (i.e. its supply chain). This study aims to discuss the relationship between CFP and sustainable supply chain, as evident in the current literature and industry practices. It provides a total comprehension of past, present and future headings in the CFP area and its contribution to a sustainable supply chain.

A systematic literature review and analysis have been undertaken in supply chain sustainability and CFP. A bibliometric approach is adopted for this investigation, and one of the biggest computerized databases, “Scopus,” has been picked. In total, 37 articles have been zeroed in after a careful and watchful screening of firmly related topics.

Most researchers gave predominance to environmental impact among the three pillars of sustainability (economy, society and the environment) for a sustainable supply chain environment. Only a few researchers were motivated to cover social development and social responsibility aspects. This review highlights how managing a CFP is one of the significant attributes of sustainable development. Existing literature in the field of CFP and sustainability have been written on actual industry cases. Food, electricity and energy are some significant industries where supply chain sustainability successfully reduces carbon emission.

The theory-building strategy with recommendations on the conceptualization of a sustainable supply chain is limited in the literature. This study gives broad ideas on how organizations modified and redeveloped different tools and technologies to make their supply chain more sustainable. The strategic role of different carbon policies, environmental rules and regulations in the domain of CFP is also recognized in this work. This study highlights the biases of most of the researches toward applications than policy interventions. This study discusses the theoretical perspective about how CFP affects supply chain management and helps organizations and researchers develop a new set of approaches in handling CFP and other sustainability aspects.

Environmental problems such as CO₂ (Carbon Dioxide) emissions have seriously affected the development of the steel industry, which has urged the industry to adopt a more effective emission reduction policy. This paper aims to analyze the impact of various CO₂ emission reduction policies combinations on the economic benefits and environmental changes of the steel industry and to determine the scope of application.

To compare the impact and applicable implementation conditions, a production decision game model that incorporates these two policies has been constructed. Short-, medium- and long-term constraints are set on the emission reduction indicators and the indicators’ changes under various scenarios are compared.

In the case of a single emission reduction policy, the carbon trading (CT) mechanism is better than the carbon tax mechanism. The mixed carbon trading mechanism is superior to the mixed carbon tax mechanism in terms of total output and subsidies, but worse in terms of overall social welfare, producer surplus and macro losses.

This paper constructs multiple emission reduction and production backgrounds and discusses the impact of the comprehensive implementation of these policies, which is practically absent in previous studies. It is in line with the current industrial policy for stable production and environmental protection and also provides a reference for the formulation of detailed policies in the future.

This study aims to explore South Korean firms’ reactions to climate change issues and the Korean emissions trading scheme (ETS) from the perspective of proactive climate-entrepreneurship. Differences in attitude toward the Korean ETS, implementation of carbon management practices and performance regarding operations, market and emission reductions are also investigated.

A research model was developed to investigate the differences in corporate perception of climate change. Using a cluster analysis and analysis of variance with 94 South Korean companies subject to the Korean ETS, the study identified carbon strategies and examined differences in characteristics among the strategies. This study undertook a robustness test by comparing the results from a large sample (n = 261) with those of the original sample (n = 94).

The study identifies four different carbon strategies based on climate-entrepreneurial proactivity: the “explorer,” “hesitator,” “attempter” and “laggard.” The “explorer” cluster is likely to have a proactive stance toward the Korean ETS regulation, while the “laggard” cluster shows resistance to this new climate policy. Entrepreneurial proactivity in carbon strategies is related to the actual adoption, implementation and effectiveness of carbon management practices.

This research is one of the few studies to explore differences in corporate response to climate change from the perspective of entrepreneurship. The study provides a theoretical foundation for extending the literature on the strategic management of climate change issues.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

Legislation plays a vital role in solving carbon emissions reduction and climate change issues. China began to implement a low-carbon economic policy in 2010, but the effect of the policy needs to be evaluated. Accordingly, this paper aims to discuss China’s low-carbon policy through exploring the following two questions, namely, whether the policy effect reaches the expected goal and whether the policy effects will balance economic development and emission reduction. Then, the paper puts forward suggestions for the improvement of China’s low-carbon policy.

This paper is organized around three distinct aspects of policy effect evaluation. This paper uses the synthetic control method to construct a policy effect evaluation model and conducts a quasi-natural experiment. The paper selects annual panel data from 2003 to 2015, which is selected from 33 provinces. A comparative analysis of carbon dioxide emissions, energy consumption and economic development between Hubei Province and Liaoning Province.

The results reveal that the implementation of the low-carbon pilot province policy in 2010 has a significant impact on the emission reduction effect of Liaoning Province, but the impact on the emission reduction effect of Hubei Province is not significant. The carbon emission trading system implemented in 2012 has reduced the emission reductions in Hubei Province and Liaoning Province has achieved better emission reduction effects after the implementation of this policy. After the implementation of the policy, the economic development of Hubei Province has been improved, but it has not brought help to the economic development of Liaoning Province. These findings provide new insights into the use of an emissions trading system for improving economic development and ultimately facilitate the attainment of the broader goal of sustainability.

This paper proposes an innovative policy effect evaluation method by considering the status of unit gross domestic product, fixed asset investment in the energy industry, energy consumption, emission reduction technology innovation and other evaluation indicators. This paper contributes to broadening current methods of policy effect evaluation in China.

The impact of volatility crush can be devastating to an option buyer and results in a substantial capital loss, even with a directionally correct strategy. As a result, most volatility plays are for option sellers, but the profit they can achieve is limited and the sellers carry unlimited risk. This paper aims to demonstrate the dynamics of implied volatility (IV) as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the exploratory factor analysis (EFA), they extract four constructs and the results from the confirmatory factor analysis (CFA) indicated a good model fit for the constructs.

This section describes the methodology used for conducting the study. This includes the study area, study approach, sources of data, sampling technique and the method of data analysis.

Although there is extensive literature on methods for estimating IV dynamics during earnings announcement, few researchers have looked at the impact of expected market maker move, IV differential and IV Rank on the IV path after the earnings announcement. One reason for this research gap is because of the recent introduction of weekly options for equities by the Chicago Board of Options Exchange (CBOE) back in late 2010. Even then, the CBOE only released weekly options four individual equities – Bank of America (BAC.N), Apple (AAPL.O), Citigroup (C.N) and US-listed shares of BP (BP.L) (BP.N). The introduction of weekly options provided more trading flexibility and precision timing from shorter durations. This automatically expanded expiration choices, which in turned offered greater access and flexibility from the perspective of trading volatility during earnings announcement. This study has demonstrated the impact of including market sentiment and liquidity into the forecasting model for IV during earnings. This understanding in turn helps traders to formulate strategies that can circumvent the undefined risk associated with trading options strategies such as writing strangles.

The first limitation of the study is that the firms included in the study are relatively large, and the results of the study can therefore not be generalized to medium sized and small firms. The second limitation lies in the current sample size, which in many cases was not enough to be able to draw reliable conclusions on. Scaling the sample size up is only a function of time and effort. This is easily overcome and should not be a limitation in the future. The third limitation concerns the measurement of the variables. Under the assumption of a normal distribution of returns (i.e. stock prices follow a random walk process), which means that the distribution of returns is symmetrical, one can estimate the probabilities of potential gains or losses associated with each amount. This means the standard deviation of securities returns, which is called historical volatility and is usually calculated as a moving average, can be used as a risk indicator. The prices used for the calculations are usually the closing prices, but Parkinson (1980) suggests that the day’s high and low prices would provide a better estimate of real volatility. One can also refine the analysis with high-frequency data. Such data enable the avoidance of the bias stemming from the use of closing (or opening) prices, but they have only been available for a relatively short time. The length of the observation period is another topic that is still under debate. There are no criteria that enable one to conclude that volatility calculated in relation to mean returns over 20 trading days (or one month) and then annualized is any more or less representative than volatility calculated over 130 trading days (or six months) and then annualized, or even than volatility measured directly over 260 trading days (one year). Nonetheless, the guidelines adopted in this study represent the best practices of researchers thus far.

This study has indicated that an earnings announcement can provide a volatility mispricing opportunity to allow an investor to profit from a sudden, sharp drop in IV. More specifically, the methodology developed by Tan and Bing is now well supported both empirically and theoretically in terms of qualifying opportunities that can be profitable because of the volatility crush. Conventionally, the option strategy of shorting strangles carries unlimited theoretical risk; however, the methodology has demonstrated that this risk can be substantially reduced if followed judiciously. This profitable strategy relies on a set of qualifying parameters including liquidity, premium collection, volatility differential, expected market move and market sentiment. Building upon this framework, the understanding of the effects of persistence and leverage resulted in further reducing the risk associated with trading options during earnings announcements. As a guideline, the sentiment and liquidity variables help to qualify a trade and the effects of persistence and leverage help to close the qualified trade.

The authors find a positive association between the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement. These findings substantiate further the four factors that influence IV dynamics during earnings announcement and conclude that just looking at persistence and leverage alone will not generate profitable trading opportunities.

The impact of volatility crush can be devastating to the option buyer with substantial capital loss, even for a directionally correct strategy. As a result, most volatility plays are for option sellers; however, the profit is limited and the sellers carry unlimited risk. The authors demonstrate the dynamics of IV as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the EFA, they extracted four constructs and the results from the CFA indicated a good model fit for the constructs. Using EFA, CFA and Bayesian analysis, how this model can help investors formulate the right strategy to achieve the best risk/reward mix is demonstrated. Using Bayesian estimation and IV differential to proxy for differences of opinion about term structures in option pricing, the authors find a positive association among the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement.