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Research on climate change mitigation has increasingly considered carbon pricing, with these efforts concentrating on reductions in carbon dioxide (CO2) emissions. Our comprehensive cross-country analysis extends this focus by quantitatively evaluating the effects of carbon pricing on four major pollutants: CO2, nitrous oxide (N2O), methane (CH4), and particulate matter (PM). We use regressions and introduce entropy balancing to this research area. Analyzing data from 132 countries from 1992 to 2019, we find that carbon pricing is associated with an average annual reduction in CO2 emissions by 3 percentage points. A one-unit increase in a coverage-weighted carbon price is associated with reductions in N2O emissions by approximately 0.1 percentage points. A shorter panel for 2010–2017 shows a larger impact of 0.3 percentage points for PM. These findings underline the efficacy of carbon pricing not just in curtailing CO2 but in significantly mitigating other harmful pollutants on a global scale. Reductions in pollutants beyond CO2 provide further motivation for policymakers to pursue carbon pricing. [ABSTRACT FROM AUTHOR]

This paper employs a propensity score matching approach to construct a control group and estimate the impact of the CETS pilot policy, a low-carbon financial policy, on corporate green innovation and its impact mechanism in a difference-in-difference manner. The results show that the CETS pilot policy has a significantly positive effect on corporate green innovation. The higher the penalty degree and the carbon price, the more obvious the promotion of the green innovation of pilot enterprises. The mechanism test shows that the improvement of corporate green innovation is mainly due to the incentive effect rather than the anti-driving effect of the CETS pilot policy, that is, the policy promotes corporate green innovation by providing innovation resources and enhancing the willingness to innovate. Further analysis shows that only in regions where local governments have less competitive pressure can the CETS pilot policy effectively promote enterprise innovation resources and that a close and clean government–business relationship can help strengthen the promotion effect of the CETS pilot policy on the willingness of enterprises to innovate. Furthermore, this paper introduces its theoretical framework as a strategic tripod to explore the friction in the process of the CETS pilot policy affecting corporate green innovation from the perspective of the industry environment and corporate resources. This research shows that a lack of industry green technology and corporate human capital may hinder the positive impact of the CETS pilot policy on corporate green innovation. Finally, this study found that the CETS pilot policy has no significant impact on the quality of corporate green innovation, and the lack of industry green technology and corporate human capital may hinder the CETS pilot policy from improving the quality of corporate green innovation. [ABSTRACT FROM AUTHOR]

This research aims to analyze the techno-economic and environmental aspects of retrofitting carbon capture and storage (CCS) technology on the existing 330 MWe pulverized coal (PC) power plant. Modeling simulations on existing PC and retrofitting PC CCS with variations in biomass co-firing (wood pellet) were carried out using the Integrated Environment Control Model (IECM) version 11.5 software. An amine-based post-combustion capture was used in this study. Coal and biomass co-firing at PC CCS reduce the net power output and thermal efficiency. Carbon neutrality occurs at 10% biomass co-firing on PC CCS. There was a 164% increase in the levelized cost of electricity (LCOE), from 0.0487 USD/kWh on PC to 0.1287 USD/kWh on PC CCS. A sensitivity analysis of fuel prices shows that at a fuel price of 25 USD/t, the LCOE of PC CCS is 0.0953 USD/kWh or higher than Indonesia's national weighted LCOE of 0.0705 USD/kWh. The LCOE of PC CCS can be lower than the national weighted LCOE when the carbon price is higher than 80 USD/t CO2. [ABSTRACT FROM AUTHOR]

The growth of the global shipping industry has increased the interest in the environmental impact of this sector. The International Maritime Organization adopted the initial Greenhouse Gas strategy for reducing GHG emissions from ships at the 72nd Marine Environment Protection Committee in April 2018. In this study, we carried out a life cycle assessment of nine production pathways of alternative fuels, including LNG, ammonia, methanol, and biofuels, and conducted an economic analysis considering the life cycle carbon pricing of each fuel pathway. Our results indicate that biomass-based FT-diesel, e-methanol, and e-ammonia are the most environmentally friendly, with GHG reductions of 92%, 88.2%, and 86.6%, respectively. However, our net present value analysis of ship life cycle cost considering carbon price indicated that using those fuels would not be cost-effective during the target period of study. Sensitivity analysis was performed by changing the life cycle carbon pricing from the baseline scenario, and we investigated the approximate years for when these alternative fuels will become more cost-effective compared to conventional fossil fuels. Further, to provide practical implications for shipping stakeholders, we analysed the effect of blending the same kinds of fuels with different production pathways. [ABSTRACT FROM AUTHOR]

The dual carbon target is a two-stage carbon reduction goal proposed by China, while the bridge maintenance strategy does not consider the need for sustainable development. Therefore, this article studies the optimization of bridge maintenance timing under China's dual carbon goals. Firstly, this paper aims to minimize the total cost of maintenance and carbon emissions, considering the continuous effects of carbon pricing and emissions in the context of the dual carbon goals. The CHINAGEM-E model is employed to predict carbon prices, and a preventive maintenance decision-making method for highway bridges is established. Secondly, based on the theory of material residual strength, a degradation model for the technical condition of highway bridges is constructed. Finally, an in-depth case analysis of an in-service highway bridge is conducted to derive optimal maintenance solutions under three scenarios. In comparison to scenarios considering only maintenance costs or those based on benchmark carbon prices, the comprehensive maintenance cost under the dual carbon targets is the highest. In the total maintenance cost, carbon emission costs constitute over 50%, emphasizing the need for increased attention to carbon emission cost studies in future maintenance research. The methodology proposed in this paper is the first to connect carbon prices with the timing of preventive maintenance for bridges, providing a more scientific and sustainable basis for future highway bridge maintenance decisions. [ABSTRACT FROM AUTHOR]

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models' resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023. [ABSTRACT FROM AUTHOR]

Under the efficient market hypothesis (EMH), part of the literature ignores the characteristics of carbon markets. Based on the fractal market hypothesis (FMH), using the MF-DCCA method, this empirical study complements related research. We studied the non-linear multifractal correlation between carbon price fluctuations and China's economic policy uncertainty (CNEPU) in Shenzhen, Beijing, Tianjin, and the national carbon market. The results show the following: (1) There is no linear correlation between price volatility and CNEPU in all carbon markets. (2) In the national carbon market, the correlation linkage between price fluctuation and CNEPU has not yet formed. (3) In the three regional carbon markets of Shenzhen, Beijing, and Tianjin, the long-range correlations exist with anti-persistence multifractal characteristics, which means that an increase in CNEPU will reduce price fluctuations. (4) After dividing the time scale into long-term and short-term, we found that it does not change the multifractal characteristics but it does change the fractal intensity. Finally, some suggestions are given to policymakers and carbon finance investors. [ABSTRACT FROM AUTHOR]

Global institutional changes (GICs), having influenced energy prices, led to a steady upward trend in carbon prices on the EU ETS. The aim of the article is to assess the changes in the relationship between carbon prices and energy prices under GICs. The Bai–Perron tests for structural breaks identified two dates as the breakpoint, 21 April 2016 and 21 September 2020. We test the hypothesis that powerful external factors (GIC) are changing the trend pattern of the carbon price time series. New pricing rules of the carbon price are being formed after the breakpoint. We use daily observations from 4 January 2010 to 1 September 2022. We use GARCH models with multiple stationary time series to discover a relationship energy price with the carbon price before and after the break points. We found that three models for two breakpoints better describe the relationship between carbon prices and energy prices than two models for one breakpoint, much less one model for the entire period. We find that the carbon price depends on energy prices, especially on the price of oil, in a statistically significant way, but the gas price is not statistically significant after 21 September 2020. [ABSTRACT FROM AUTHOR]

Using the event study methodology, the paper studies the effects of 22 key events in countries' process of entering and exiting the European Union on returns of European Union Allowance (EUA) future prices in the EU Emissions Trading System (EU ETS). The events include 17 entry events concerning the signing of relevant agreements, becoming a candidate or potential candidate country, the process of a negotiation and formally entering the EU, and five exit events including the process of Brexit and the suspension of Iceland. The results show that two entry events involving Albania and Ukraine, respectively, have a significant positive impact, and five entry events have a significant negative impact. Among the exit events, the announcement of the Brexit referendum results causes significant negative market reaction. Most events regarding small carbon emitters entering the EU lead to negative cumulative abnormal returns (CAR) of EUA prices, and a significant negative correlation between the countries' annual average carbon emissions and CAR is found, while the change of national allocation plans does not affect the market reaction notably. In the process of establishing a unified carbon market, regulators should carry out appropriate policy arrangements of emission allowances allocation when new members join, in order to guide market expectations and enhance market stability. [ABSTRACT FROM AUTHOR]

The cement industry is a major contributor to greenhouse gas emissions on a global scale. Consequently, there has been an increasing interest, in both academia and business, in low-carbon concretes in which Ordinary Portland Cement (OPC) is partially or fully replaced with industrial side streams. However, the realization of the environmental benefits of such materials depends on how competitive they are in the construction market, where low costs are a major competitive factor. This is not straightforward, as many types of concretes exist. Raw material prices vary, and costs can be influenced by governmental regulations via carbon pricing. This study presents a case study estimating the cost prices of four different geopolymer concretes with different material compositions and carbon footprints, considering the raw material price variability and the potential impact of carbon emissions regulation (carbon price). The case study demonstrates how material mix cost comparisons can be made openly and systematically. The results imply that carbon pricing, at the rates currently applied, does not significantly change the cost price difference between traditional and geopolymer concretes. Instead, cost-competitiveness of low carbon concretes depends heavily on the material mix type and the availability of critical side streams. [ABSTRACT FROM AUTHOR]

I estimate the cost of meeting the EU 2030 targets for greenhouse gas emission reduction, using statistical emulators of ten alternative models. Assuming a first-best policy implementation, I find that total and marginal costs are modest. The statistical emulators allow me to compute the risk premiums, which are small, because the EU is rich and the policy impact is small. The ensemble of ten models allows me to compute the ambiguity premium, which is small for the same reason. I construct a counterfactual estimate of recent emissions without the climate policy and use that to test the predictive skill of the ten models. The models that show the lowest cost of emission reduction also have the lowest skill for Europe in recent times. [ABSTRACT FROM AUTHOR]

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD), genetic algorithm (GA) and artificial neural network (ANN) is proposed to forecast carbon price. Firstly, the proposed model uses EMD to decompose carbon price data into several intrinsic mode functions (IMFs) and one residue. Then, the IMFs and residue are composed into a high frequency component, a low frequency component and a trend component which have similar frequency characteristics, simple components and strong regularity using the fine-to-coarse reconstruction algorithm. Finally, those three components are predicted using an ANN trained by GA, i.e., a GAANN model, and the final forecasting results can be obtained by the sum of these three forecasting results. For verification and testing, two main carbon future prices with different maturity in the European Climate Exchange (ECX) are used to test the effectiveness of the proposed multiscale ensemble forecasting model. Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW), ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARIMA model with EMD preprocessing. [ABSTRACT FROM AUTHOR]

Emissions trading schemes (ETSs) are effective measures that facilitate economic growth and carbon mitigation, especially for developing countries such as China. These schemes can further affect the cash flow, production, and investment decisions of regulated companies. However, few empirical studies have explored how ETSs promote companies' market value. We systematically evaluate the influence of the carbon emission trading (CET) policy on companies' market value and explore the influential mechanism. We use the data of listed companies from the Chinese stock "A" markets and employ the difference-in-difference method to account for the unobserved cause of the CET policy regarding companies' market value. Robust benchmark regression results reveal that the CET policy promotes companies' market value significantly. The mechanism analysis reveals that the CET policy can improve the market value of listed companies by influencing the carbon price, innovative activities, and carbon disclosure. The results of the heterogeneity analysis show that the CET policy's impact on companies' market value is heterogeneous in terms of marketization degree, industry, firm ownership, and different regions. We suggest that the carbon pricing mechanism, degree of market perfection, carbon disclosure policy, and carbon finance should be optimized to improve the efficiency of ETSs. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity. [ABSTRACT FROM AUTHOR]

This paper presents a review of literature on the development of climate economy models in Malaysia from 1988 to 2020. The type of climate economic models used in Malaysia and their attributes were analyzed. The key attributes reviewed include functions, capability, the various approaches, types of Greenhouse gas (GHG) emissions, and various sectors included in the modelling. The review analysis revealed that climate economic modelling in Malaysia can be categorized into two groups namely Input-Output (I-O) table and Social Accounting Matrix (SAM) models as well as the Integrated Assessment Models (IAM). Both groups of climate economic models complement each other in functional capability to do top-down or bottom-up as well as short-term and long-term analysis. The key contribution of this review is in discovering three key gap clusters that exist in climate economic modelling in Malaysia. These gaps include the coverage on types of GHGs, disclosing the GHG sector's coverage in the modelling, and the discovering of the exclusion of climate damage functions as well as the unavailability of customized functions to suit Malaysia's climate and geography. These three key gaps need to be urgently addressed for effective policy interventions in Malaysia and to meet the global goals of the Paris Agreement. [ABSTRACT FROM AUTHOR]

The Chinese government has made a commitment to control carbon emissions, and the deployment of renewable energy power generation is considered as an effective solution. In recent years, great effort has been exerted to support the development of renewable energy in China. While, due to fiscal pressures and changes in management policies, related subsidies are diminishing now and energy users are asked to pay for the cost. Regulations about carbon cap and renewable energy consumptions are issued to transfer the responsibility of consuming renewable energy and reducing carbon emissions to energy consumers. A national carbon trading system is set up in China and is under its growth stage. Therefore, this study lists the factors that should be considered by the energy users, analyzes the levelized cost of electricity generated by renewable energy in four cities in China, Beijing, Shanghai, Guangzhou, Wuhan, and compares the results with current carbon prices. Based on the research, under the current status, it is still more cost-efficient for enterprises to buy carbon credits than introduce renewable energies, and great differences among cities are shown due to different natural conditions. Besides, with diminishing subsidies and development of the carbon trading market, the carbon price will gradually reflect the actual value and carbon emission reduction costs will become an important part of enterprise expenditure. In the long term, enterprises should link more factors to carbon emissions, like social responsibility and brand image, instead of only the cost. [ABSTRACT FROM AUTHOR]

Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can't perform well. Furthermore, the semiparametric model doesn't need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China's emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China's emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks. [ABSTRACT FROM AUTHOR]

This paper examines the price of carbon emission rights published by the China Emissions Exchange (Shenzhen), analyzes the statistical characteristics of the price series and uses a generalized autoregressive conditional heteroskedasticity (GARCH) model to describe the price fluctuation of carbon emission rights and risk formation mechanisms. The study shows the following results: since 2013, China's carbon emission rights prices have become more stable. The fluctuation of yield has gradually decreased and the market has approached a more mature stage. However, after 2018, due to factors such as the economic downturn and insufficient market information, the amplitude of price fluctuations has started to rise while frequency is increasing, which shows an asymmetry trend. The market trading risk is accumulating constantly. [ABSTRACT FROM AUTHOR]

Afforestation programs have huge potential to store carbon, thereby contributing to mitigate climate change. However, the voluntary acceptance by landowners crucially depends on their economic outcome. We (i) assess the carbon sequestration potential of afforestation projects in various Chinese regions by comparing different tree species, project durations and regional particularities, (ii) analyze the costs and benefits of tree species used for timber and fruit production as well as bamboo, and (iii) compare them with alternative crops under different climatic and economic conditions. Finally, we calculate the minimum compensation required by farmers to convert cropland to forests assuming a joint production of timber or fruits and carbon sequestration. No compensation is needed when cropland with relatively low revenues is to be converted. In contrast, compensation payments must be high for converting land used for cash crops, such as sugarcane. For fruit trees, the amount of carbon sequestered is low, but selling fruits and nuts is quite profitable. Consequently, in most cases there is no compensation needed. The minimum price per carbon credit decreases with increasing project duration because more carbon is stored per hectare, and in consequence, the required payments per credit are lower. This does not hold for fast-growing trees like eucalyptus and poplar, for which the minimum price increases with extended project duration. Bamboo shows a high carbon sequestration potential, and becomes economically more attractive by including carbon revenues. Carbon credits can often compensate the opportunity costs of alternative land uses, except for afforestation on highly productive cropland. We demonstrate that the economic attractiveness of afforestation projects is strongly context dependent and varies substantially across regions. The findings can contribute to the cost-efficient design of carbon sequestration projects. The methodology can be applied to other regions in the developing world. [ABSTRACT FROM AUTHOR]

The carbon market is the least-cost tool to reduce carbon emissions. This study explores the evolution of the carbon price in the carbon market from a dynamic system perspective. A three-dimensional carbon price dynamic system is established to quantify the interactions among the carbon price, energy price, and economic growth. The system built in this study presents various dynamic characteristics including chaotic attractors and stable equilibria. Specifically, the existence of chaos in the system is verified by Lyapunov exponents spectrum and bifurcation diagram. In contrast, the system tends to be stable in the case of China after identifying the system parameters through the genetic algorithm. Furthermore, evolutionary trends of the carbon price are analyzed when the system parameters are perturbed. The results show that the carbon price is positively correlated with energy price as well as energy price policy. Besides, the level of the carbon price is negatively correlated with government control in the short term and positively correlated in the long term. This study can help analyze trends in the carbon price in the mid-term to long-term. [ABSTRACT FROM AUTHOR]

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels, and related obsolescence costs. These choices have an economic impact on the supply chain, in terms of different logistic costs, and an environmental impact, in terms of the carbon emissions produced during goods transportation, storage and final recovery. In this paper, we initially analyze and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we overcome a traditional single objective formulation, by developing a bi-objective lot-sizing model in which costs and emissions are kept separated and analyzed by using a Pareto frontier subject to a Cap and Trade mitigation policy. The model is useful in practice to support managers in understanding the Pareto frontier shape linked to a specific purchasing problem, defining the cost-optimal and emission-optimal solutions and identifying a sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyze the model behavior according to variation in market carbon price and we finally analytically demonstrate that today carbon prices are still far too low to motivate managers towards sustainable purchasing choices: there is still a gap of about 79%. [ABSTRACT FROM AUTHOR]

This article studies the relationship between the prices of fuel and EU Allowances (EUA) for carbon emissions during Phase 3 of the European Union Emissions Trading System. We find that the forward prices of EUA, coal, gas and Brent oil are jointly determined in equilibrium. The existence of such a long-run relationship entails a permanent-transitory decomposition for the series of EUA and fuel prices that reveals the short- and long-term causal influence of the EUA market in shaping the joint dynamics of fuel prices. This result complements the literature that suggests that EUA prices are driven by the dynamics of fuel prices. Interestingly, we do not find an equilibrium relationship in the spot market. EUA and fuel spot prices are driven by independent unit root processes. The differences between spot and forward markets are attributed to the tradability of forward prices that are used for speculation and hedging in financial markets. In contrast, spot prices are mainly driven by supply and demand in energy markets. [ABSTRACT FROM AUTHOR]

The Kyoto Protocol came into effect in 1997 to curb greenhouse gas (GHG) emissions and to address the problem of climate change. The Protocol includes a market-based mechanism designed to offset GHG emissions, called the emissions trading scheme (ETS), allowing companies to "trade" their shortage or surplus allowance. This study examines the determinants of the EU allowance (EUA) price in Phase 3 of the EU ETS (2013–2017). First, the causality between the EUA price and other variables is determined using a Granger causality test. Second, the correlation between the EUA price and each variable is measured using a VECM estimation and an impulse response function. Finally, the relative effect of each variable on the EUA price is determined using a forecast error variance decomposition. The results show that the EUA price has a causal effect on the prices of electricity and natural gas. Second, all variables, except the minimum temperature, show a positive relationship with the EUA price. Furthermore, when unexpected shocks occur, the EUA price shows the highest response to its past price, followed by the electricity price. Third, the past EUA price has the most influence on the EUA price, followed by the coal price. [ABSTRACT FROM AUTHOR]

The emission trading scheme (ETS) has become a significant tool to solve the climate change problem. China has built domestic carbon trading pilots to control energy consumption and reduce emissions. This paper explores the linkage between the carbon market and covered corporate value in China. To address the relationship, this paper estimates the impact that the carbon prices of different pilots in China have on the value of thermal listed enterprises and the extent of this impact. By using weekly data from July 2014 to June 2017, we analyze the overall effect and perform a comparative study of influences of the three trading years. Moreover, we test if the effect of carbon trading pilots on electricity corporate value is market-specific. The results demonstrate that carbon prices have a significantly negative impact on stock value when looking at the full sample and the effects vary between markets. [ABSTRACT FROM AUTHOR]

Verified emissions announcements are the most influential events in the European Union emissions trading scheme (EU ETS); they reveal demand information and have a significant impact on the carbon market. The extant literature tends to focus on examining the impacts of these verification events on the prices of carbon allowances, while scholars barely discuss how trading behaviors react to the announcements. Moreover, most of the studies are carried out from a macroeconomic perspective. This paper fills this gap by analyzing the impacts of the verified emissions announcements on the comoves of trading behaviors and carbon prices in Phase I (2005–2007) and Phase II (2008–2012). Specifically, we construct GARCH models to investigate the events' heterogeneous influences in different periods, i.e., the complete periods, the announcement periods, the pre- and post-announcement periods. We observe that the verified emissions announcements boost the volume of compliance trading, particularly in Phase I. Furthermore, we show that the over-allocation of carbon allowances can be even more influential in disturbing the comoves than the verification events. Our microeconomic findings confirm the maturity of EU ETS in Phase II, exhibiting good agreement with the extant macroeconomic literature. [ABSTRACT FROM AUTHOR]

Refurbishing buildings helps reduce waste, and limiting the amount of embodied carbon in buildings helps minimize the damaging impacts of climate change through lower CO2 emissions. The analysis of embodied carbon is based on the concept of life cycle assessment (LCA). LCA is a systematic tool to evaluate the environmental impacts of a product, technology, or service through all stages of its life cycle. This study investigates the embodied carbon footprint of both new-build and refurbished buildings to determine the embodied carbon profile and its relationship to both embodied energy and construction cost. It recognizes that changes in the fuel mix for electricity generation play an important role in embodied carbon impacts in different countries. The empirical findings for Hong Kong suggest that mean embodied carbon for refurbished buildings is 33–39% lower than new-build projects, and the cost for refurbished buildings is 22–50% lower than new-build projects (per square meter of floor area). Embodied carbon ranges from 645–1059 kgCO2e/m2 for new-build and 294–655 kgCO2e/m2 for refurbished projects, which is in keeping with other studies outside Hong Kong. However, values of embodied carbon and cost for refurbished projects in this study have a higher coefficient of variation than their new-build counterparts. It is argued that it is preferable to estimate embodied energy and then convert to embodied carbon (rather than estimate embodied carbon directly), as carbon is both time and location specific. A very strong linear relationship is also observed between embodied energy and construction cost that can be used to predict the former, given the latter. This study provides a framework whereby comparisons can be made between new-build and refurbished projects on the basis of embodied carbon and related construction cost differentials into the future, helping to make informed decisions about which strategy to pursue. [ABSTRACT FROM AUTHOR]