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This work outlines major critical physico-chemical parameters that play a key role in increasing the fixation of CO2 from coal-fired flue gas CO2 into green microalgae biomass. Nitrogen concentration, gas flow rate, initial medium pH, and incident light intensity were determined to be the most important process variables with significant impact on CO2 fixation. Therefore, NaNO3 (500-3000 mg L-1), pH (6.8-8.0), light (50-200 mol m-2 s-1) and aeration (0.1-1.0 vvm) were varied to assess the biological assimilation potential of CO2 from the flue gas. The parameters that resulted in maximal CO2 fixation from raw flue gas, resulting in a maximum biomass density of 3.1 g L-1, were NaNO3 = 1500 mg L-1, pH =7.2-7.5, incident light intensity = 133.33 mol m-2 s-1, and 0.5-0.75 vvm aeration without any cost-incurring flue gas pre-treatment step. The inductively coupled plasma-mass spectrometer (ICP-MS) was used to investigate heavy metals uptake from raw flue gas, and it was discovered that no net intake of trace metals had a significant influence on biomass production. The research lays the path for efficient large-scale microalgal cultivations for industrial uses, as well as bolstering the circular economy concept.

The massive accumulation of red gypsum has brought great harm to human and environment. In order to achieve low-cost and high-benefit resource utilization of red gypsum, a method of metal self-enrichment during the cycle and transformation of red gypsum was proposed. The carbon dioxide and ammonia water react with red gypsum to manufacture ammonium sulfate and solid phase products. And solid phase products neutralize pickling wastewater to produce metal-enriched red gypsum for the next cycle. After the cycle, solid phase product with one circulation (containing 39.45% Fe and 7.37% Ca) and two circulation (containing 45.79% Fe, 4.75% Ca) could be obtained, which can be used as the proportioning of metallurgical sinter. The self-enrichment process not only realizes the comprehensive utilization of red gypsum, but also solves the treatment of acidic wastewater.

Biochar produced from pyrolysis of biomass is a candidate with great potential for climate change mitigation by carbon sequestration and reduction of greenhouse gases (GHG) emission in soil. Its potential depends considerably on biochar properties. Biochar stability or biochar C recalcitrance is decisive to its carbon storage/sequestration potential in soil. Three groups of methods including: I) biochar C structure or composition analyses, II) biochar oxidation resistance determination, and III) biochar persistence assessment by incubation & modelling, have been developed for evaluation of biochar stability. Amongst, incubation & modelling is the most commonly used one and is the basis of the other two assessment methods. However, the strategies for incubation experiment designing and data modelling significantly influence the biochar stability results. Drastic differences were observed for stability results obtained from different studies partly because of the large flexibility of the incubation & modelling method. Biased biochar stability would be obtained if the method was used improperly. The present review aims to provide comprehensive information on method strategies used for incubation and modelling, followed by discussions on the key issues such as what kind of biochar to use, how the experiment should be designed, how to determine biochar C mineralization, how the mineralization data should be expressed, and what model should be used, for an accurate biochar stability evaluation. In general, incubating biochar at long-term duration, modelling incubation data with double-exponential model, using C isotopic technology for CO2 evolution determination with C mineralization data express as percentage of total organic carbon mineralized, applying biochar in the field are favorable to biochar stability assessment. Other strategies such as the use of standard (reference) biochar materials may be effective to improve the assessment.

Aquaculture is one of the fastest-growing segments of the food economy in modern times. It is also being considered as an important source of greenhouse gas (GHG) emissions. To date, limited studies have been conducted on GHG emissions from aquaculture system. In this study, daily addition of fish feed and soluble starch at a carbon-to-nitrogen (C/N) ratio of 16:1 (w/w) was used to examine the effects of carbohydrate addition on nitrogen transformations and GHG emissions in a zero-water exchange intensive aquaculture system. The addition of soluble starch stimulated heterotrophic bacterial growth and denitrification, which led to lower total ammonia nitrogen, nitrite and nitrate concentrations in aqueous phase. About 76.2% of the nitrogen output was emitted in the form of gaseous nitrogen (i.e., N2 and N2O) in the treatment tank (i.e., aquaculture tank with soluble starch addition), while gaseous nitrogen accounted for 33.3% of the nitrogen output in the control tank (i.e., aquaculture tank without soluble starch addition). Although soluble starch addition reduced daily N2O emissions by 83.4%, it resulted in an increase of daily carbon dioxide (CO2) emissions by 91.1%. Overall, starch addition did not contribute to controlling the GHG emissions from the aquaculture system.

Urbanization and population aging are key indicators of human-related social attributes. With economic progress and urban development, human living conditions and the level of medical and health care have been continuously improved. Population aging has become a global trend, which brings serious challenges to the world. Environmental sustainability is closely linked to both urbanization and aging. Most of the existing studies only focus on the linear relationship between urbanization and the environment, and the effect of aging on the ecological environment is also controversial. It is of great significance to conduct systematic research on urbanization-aging-environment. This paper aims to reconstruct the linear relationship between urbanization and the environment, investigating the nonlinear effect of population aging on the nexus of urbanization-environment in 156 countries. This paper focuses on exploring ways to improve environmental quality from the perspective of population aging. To this end, the panel threshold regression models of urbanization‑carbon emissions and urbanization-ecological footprint are developed respectively. In which, urbanization is set as the explanatory variable, carbon emissions and ecological footprint are set as the explained variables, and population aging is set as the threshold variable. This paper divides four income groups according to the income standard of the World Bank, and regresses the panel data of the global and four income groups respectively to reflect the comprehensiveness of this work. The results show that there is a threshold effect of population aging on the nexus of urbanization‑carbon emissions/ecological footprint both the global scale and different income groups. On a global scale, urbanization has a positive effect on carbon emissions and ecological footprint. When aging crosses the threshold in turn, the promotion effect of urbanization on carbon emissions gradually becomes smaller, and the influence coefficient of urbanization on the ecological footprint shows an inverted U-shaped change trend. Aging can reduce the environmental pressures related to urbanization. There is heterogeneity in the nonlinear regression results for different income groups. Population aging variable in high income group, upper middle income group helps to improve environmental quality. In lower middle income countries and low income countries, aging slightly increases the coefficients of urbanization and ecological footprint.

This paper studies the impact of global value chains (GVCs) participation on carbon emissions embodied in exports (EEE) of China during 2005-2016, and analyses firm heterogeneity from the perspective of firm ownership and trade patterns; Then, industries are classified based on factor intensity and technology level, and the industrial heterogeneity of different firms is analyzed; Finally, through counterfactual analysis, this paper evaluates EEE under four anti-globalization scenarios. The empirical study shows that with the increase of GVCs position, EEE first increases and then decreases, showing an inverted U-shaped distribution. From the perspective of firm ownership, the relationship between GVCs position of domestic firms and EEE is inverted U-shaped distribution, while GVCs position of multinational enterprises (MNEs) is positively correlated with EEE. The relationship of capital-intensive and technology-intensive domestic firms, high-tech and medium-tech manufacturing domestic firms are also inverted U-shaped distribution. The positive correlation of MNEs is reflected in the low-tech manufacturing industries. From the perspective of trade patterns, with the increase of GVCs position, EEE of processing trade firms presents an inverted U-shaped distribution, while it is not significant for general trade firms. The inverted U-shaped relationship between GVCs position in capital-intensive industries and EEE are confirmed in all firms. Under anti-globalization scenarios of 50% backflow, EEE would increase. Under scenarios of 100% backflow, EEE would reduce. The results above provide useful references to achieve carbon emission reduction targets.

Carbon trading scheme is an instrument adopted in many countries of the world to reduce CO2 emissions. As an important way of environmental regulation, whether it can reduce the emissions and promote the economic development at the same time needs further investigation. This paper tests whether the Porter Hypothesis is true in China's carbon emissions trading scheme for energy intensive industries. Using provincial-level, industrial-level and firm-level data, we construct a DEA model that can incorporate the emissions trading behavior among different decision making units to show that the carbon emissions trading scheme can only reduce the CO2 emissions but cannot increase the output significantly. That is, the carbon intensity is decreased. The reason is that the carbon trading scheme is conducive to the improvement of the production efficiency, and firm-level research and development input increases after carbon trading scheme. These findings are robust to several robustness checks. Our paper demonstrates the effectiveness of the carbon emissions trading scheme in reducing emissions. An external technological breakthrough is needed if the win-win situation of reducing CO2 emissions and promoting economic development simultaneously is wanted to be achieved.

The 45th presidential administration declared natural gas to be “molecules of freedom”, a perfunctory used for political gain. Additionally, this administration has also levied tariffs as a political tool designed to reduce trade deficits rather than promote sustainability. In this paper, we study the impact of tariffs on domestic carbon intensities and carbon dioxide emissions. We find that tariffs increase carbon intensities and do not have an effect on carbon dioxide emissions. Since tariffs also reduce output, we conclude that by implementing tariffs the second best problem is invoked and overall market inefficiency increases. Additionally, since carbon intensities rise and overall carbon dioxide emissions do not decline, regional concerns over environmental quality and health should be raised. The paper concludes with a discussion of how these results impact policy decisions pertaining to instruments for promoting sustainability.

Carbon dioxide (CO2) caused by residential building sector is essential for China to achieve its 2030 carbon emission peak. As the indicator to control the total CO2 emissions, CO2 intensity feature should be preferentially analyzed. Through the decomposition and decoupling approaches, this paper is the first to investigate whether CO2 intensity decouples from the income level in residential building sector of China and its four megacities [Beijing (BJ), Tianjin (TJ), Shanghai (SH), and Chongqing (CQ)] from 2000 to 2016. Major findings indicate that: (1) The nationwide status was the weak decoupling during 2001–2016 excluding 2003 (the expansive coupling) and 2014 (the strong decoupling). (2) At the megacity scale, only four decoupling levels appeared and the decoupling effect rank of four megacities is as follows: CQ > TJ > BJ > SH (2001–2004), BJ > TJ > CQ > SH (2005–2008), SH > BJ > TJ > CQ (2009–2012), and SH > BJ > TJ > CQ (2013–2016). (3) Carbon Kuznets curves explained the different decoupling modes of four megacities and the increasing decoupling effects in 2001–2016 are attributable to the implementation of energy conservation strategy. Overall, this paper fills the gap of decoupling CO2 intensity from the income level in residential building sector. Furthermore, a significant contribution is made by this paper to the carbon emission peak analysis in residential building sector of China, and our efforts will also be a case for other countries and cities to evaluate the decoupling effect in their residential building sectors.

China's carbon emissions have developed swiftly in recent decades, which will not only affect the nation's own sustainable development, but have a potentially negative impact on global climate stability. Given that socioeconomic development is susceptible to regional heterogeneity and geographic scales, a systematic exploration of spatiotemporal variations of carbon emission intensity (CEI) and their drivers across different levels is conducive to enacting more reasonable and efficient measures for emission reduction. However, there is still a lack of comprehensive analysis of these issues. In this paper, we attempted to quantify and compare the spatiotemporal evolution and spatial spillover effects of impact factors on CEI from nighttime light imagery and socioeconomic data at two China's administrative levels by utilizing the variation coefficient, spatial autocorrelation model and spatial econometric methods. The results showed that the spatiotemporal variations of CEI were greater at the prefecture level compared to the provincial level during 2000-2017. There were significant positive spatial autocorrelation of CEI at two administrative levels, and self-reinforcing agglomeration was more substantial at the prefectural level than that provincial level. While the local spatial clustering of CEI of each administrative level altered with scale dependence, the binary spatial structure (High-High and Low-Low) of CEI remained relatively steady in China. Various driver factors not only had direct effects on local CEI, but had spatial spillover effects on neighboring areas. Our findings illustrate that China's CEI is sensitive to the space-time hierarchy of multi-mechanisms, and suggest that "proceed in the light of local conditions" strategies can assist the Chinese government for CEI mitigation.

China faces enormous pressure to reduce carbon emissions. Since the agglomeration and driving effect of urban agglomerations have continued to increase, relying on the network relationship within urban agglomerations to coordinate emission reduction becomes an effective way. This paper combines the modified Gravity model and Social Network Analysis method to measure the structure characteristics of carbon emission spatial correlation network of the seven urban agglomerations as a whole and each urban agglomeration in China, analyzes the interaction mechanism between cities and between urban agglomerations, and finally explores the influencing factors of carbon emission spatial correlation through the QAP analysis method. The results are as follows: (1) As for the overall network, overall scale was increasing, but the hierarchical structure had a certain firmness. YRD and PRD urban agglomerations were at the center of the network and received the spillover relationship of MRYR, CC, CP, and HC urban agglomerations. (2) As for the networks of urban agglomerations, the allocation of low-carbon resource elements still needed to be optimized, especially BTH urban agglomeration. Beijing, Shanghai, Nanjing, Wuxi, etc. were at the center of the network. The influencing factors and degree of carbon emission spatial correlation in each urban agglomeration were different.

The comprehensive consideration of climate warming and by-product management in the iron and steel industry, has a significant impact on the realization of environmental protection and green production. Blast furnace slag (BFS) and steel slag (SS), collectively called iron and steel slags, are the main by-products of steelmaking. The economical and efficient use of iron and steel slags to reduce greenhouse gas (GHG) emissions is an urgent problem to be solved. This paper reviewed the carbonization and waste heat recovery of iron and steel slags, and the utilization of iron and steel slags as soil amendments, discussed their application status and limitations in GHG reduction. Iron and steel slags are rich in CaO, which can be used as CO

Since China's announcement of the Belt and Road Initiative (BRI) in 2015, much focus has been drawn on the environmental impacts of China's energy investments in the countries along the BRI. The economic and social impacts of these investments, which are also important for the wellbeing for local people, left largely uninvestigated. In this paper, we used China's renewable energy investments in Pakistan as a case study to investigate the contributions of these investments on local economy and employment. Through IO table analysis, we found that the 28 renewable power plant projects invested by China till now potentially provided 8905 jobs and generated around USD 39.8 million production values in related sectors in Pakistan, including USD 30.7 million from wind power plants development and 9.1 million from solar. When Chinese companies act as engineers and constructors, the increase of production value in relevant sectors in Pakistan (USD 6.05 million per 100 MW) are higher than wind power plant projects with other magnitude of engagement (3.82 million as a fully sponsor, 4.19 million as only finance supporter and 2.29 as equipment provider). Wind power plants will create more jobs and increase more production values than solar power plants. This study identifies the economic and social benefits of BRI renewable energy investments from China and the driving mechanism, thus providing basis for promoting renewable energy investments in countries like Pakistan so that they can gain new drive for social and economic growth from the global trend of low carbon transition.

With the growing development of hydropower projects all over the world, the excessive greenhouse gas (GHG) emissions from increasing reservoirs have drawn public concern. While precise evaluations of GHG emissions are urgently needed, the widely applied methods including floating chamber (FC) method and thin boundary layer (TBL) method are unsatisfactory. In this paper, a new methodology of estimating CO

Both high carbon emission intensity (CEI) and large scale of shadow economy in China are the undesirable products of economic development with too fast growth rate. For the rapid and healthy development of the economy in China, the research on the relationship between the two should attract more attention, while the relevant literatures are very few at present. According to the panel data of 30 provinces in China from 2004 to 2016, this paper firstly examines the spatial correlation between CEI and the scale of shadow economic. Then verifies the interaction relationship between them with SPDM (spatial panel Dubin Model). Moreover, the robustness test is conducted with three different spatial weight matrices. As the interaction between CEI and shadow economy has been proved, providing new ideas for carbon emission reduction, environmental protection, and healthy economic development with rapid rate in the future. The specific conclusions are as follows: first, CEI and shadow economy both have significant positive spatial autocorrelation. Second, there is a spatial interaction between CEI and shadow economy, indicating provincial cooperation plays a very important role in both economic growth and environment protection. Third, the impacts from economic development on both CEI and shadow economy satisfy the EKC hypothesis. Also, the development of the tertiary industry plays a positive role in promoting the growth of CEI, while promotes and inhibits the expansion of shadow economic scale at the same time.

This review paper aims to identify the main sources of carbon dioxide (CO2) emissions from wastewater treatment plants (WWTPs) and highlights the technologies developed for CO2 capture in this milieu. CO2 is emitted in all the operational units of conventional WWTPs and even after the disposal of treated effluents and sludges. CO2 emissions from wastewater can be captured or mitigated by several technologies such as the production of biochar from sludge, the application of constructed wetlands (CWs), the treatment of wastewater in microbial electrochemical processes (microbial electrosynthesis, MES; microbial electrolytic carbon capture, MECC; in microbial carbon capture, MCC), and via microalgal cultivation. Sludge-to-biochar and CW systems showed a high cost-effectiveness in the capture of CO2, while MES, MECC, MCC technologies, and microalgal cultivation offered efficient capture of CO2 with associate production of value-added by-products. At the state-of-the-art, these technologies, utilized for carbon capture and utilization from wastewater, require more research for further configuration, development and cost-effectiveness. Moreover, the integration of these technologies has a potential internal rate of return (IRR) that could equate the operation or provide additional revenue to wastewater management. In the context of circular economy, these carbon capture technologies will pave the way for new sustainable concepts of WWTPs, as an essential element for the mitigation of climate change fostering the transition to a decarbonised economy.

The combustion of fossil fuels produces a large amount of carbon dioxide (CO2), which leads to global warming in the world. How to rationally consume fossil energy and control CO2 emissions has become an unavoidable problem for human beings while vigorously developing economy. This paper proposes a novel economy and CO2 emissions prediction model using an improved Attention mechanism based long short term memory (LSTM) neural network (Attention-LSTM) to analyze and optimize the energy consumption structures in different countries or areas. The Attention mechanism can add the weight of different inputs in the previous information or related factors to realize the indirect correlation between output and related inputs of the LSTM. Therefore, the Attention-LSTM can allocate more computing resources to the parts with a higher relevance of correlation in the case of limited computing power. Through inputs with the consumption of oil, natural gas, coal, hydroelectricity and renewable energy, the desirable output with the per capita gross domestic product (GDP) and the undesirable output with CO2 emissions prediction model of different countries and areas is established based on the Attention-LSTM. The experimental results show that compared with the normal LSTM, the back propagation (BP), the radial basis function (RBF) and the extreme learning machine (ELM) neural networks, the Attention-LSTM is more accurate and practical. Meanwhile, the proposed model provides guidance for optimizing energy structures to develop economy and reasonably control CO2 emissions.

Decoupling economic growth from emissions is vital to achieve the environmental targets postulated by the Paris agreement and the Sustainable Development Goals. This paper analyzes a set of factors that have the potential to increase the rate of emissions decoupling in 35 OECD countries 1994-2016. It takes on an encompassing approach focusing on emissions decoupling from two pollutant types carbon dioxide (CO

Climate change caused by rapid increases in greenhouse gas concentrations is now a global challenge. Foreign direct investment (FDI), as a key player in global economic growth, is a major contributor to carbon emissions. Based on panel data of 30 provinces in China collected from 2007 to 2018, this paper uses the two-tier stochastic frontier model to analyse the opposing two-sided effects of FDI on carbon emission performance and calculates their combined effects. The empirical study shows that FDI has both a promoting and an inhibiting effect on carbon emission performance, and the overall effect is characterized by less of an inhibiting effect than a promoting effect, resulting in the positive driving characteristic of the combined effect. The average inhibiting effect is 0.0402, and the average promoting effect is 0.1065, causing the comprehensive effect of FDI on carbon emission performance to have an average value of 0.0663. The empirical results also show that the promoting effects of FDI are greater than the inhibiting effects in most regions; however, after 2013, the level of the promoting effect declined overall. There are regional differences in the combined effects of FDI on carbon emission performance, and the driving effects present in the central and western regions of China are significantly lower than that in the eastern region. Based on the research conclusions, while promoting a "green" revolution in FDI utilization patterns, it is necessary to strengthen the interaction mechanism between FDI and low-carbon economic development in China and to enhance the driving effect of FDI on carbon emission performance in China.

Subsurface elevated temperatures (SETs) often occur in landfills and pose great threats to their structural and environmental integrity. Current landfill gas monitoring practices only recommend maintaining certain soil gases percentages, with no integrated strategy for predicting subsurface temperature. As a solution, this paper proposes a comprehensive risk assessment framework specific to SET mitigation. The risk model (RSET) was constructed by incorporating independent gas variables (methane, carbon dioxide, oxygen, residual nitrogen, and temperature) identified in the existing literature as SET indicators, and analyzing gas-well data from the Bridgeton Landfill. Upon identifying these gas indictors and their safety thresholds, we found a significant association (p-value < 0.05) between safe-unsafe ranges of gas variables and subsurface temperature. Temperatures above 80 °C were found to be associated with 100%, 92.3%, and only 4% of the unsafe ranges of methane, residual nitrogen, and oxygen, respectively. As the correlation between gases and temperature seemed to vary for different gas combinations, we developed the RSET by incorporating into these correlation coefficients event intensities specific to certain gas combinations, and then normalizing the RSET scale over a 0-10 range. Over the study period, we identified 22.29% of cases as medium risk at the Bridgeton Landfill and 17.7% as high risk. SETs are governed by different combinations of safe-unsafe ranges of parameters rather than any individual parameters alone. Subsequently, we used a decision tree algorithm to assess the risk types associated with RSET values. The proposed RSET can serve as a monitoring and decision-making tool for landfill authorities for managing and preventing SET incidents.

The aim of this study was to quantify and compare greenhouse gas (GHG) (i.e. carbon dioxide (CO2), nitrousoxide (N2O) and methane (CH4)) emissions from two full-scale winery wastewater and sludge treatment sys-tems (i.e. constructed wetlands (CWs) and activated sludge system) located in Galicia (Spain). GHG fluxeswere measured using the static chamber method in combination with an on-site Fourier transform infrared spectroscopy (FTIR) gas analyser in the CWs system. These on-site innovative techniques proved to be very accurateand reliable. In the activated sludge treatment systems, thefloating chamber method in combination with the FTIR gas analyser was used. Measurements were carried out during the vintage season, when winery wastewaterhas the highestflow and loads, and the rest of the year. Emission rates of CO2, N2O and CH4 in the CWs units(i.e. vertical flow, horizontal subsurface flow and sludge treatment wetlands) ranged from 1.35E+02 to 7.54E+04, 1.70E-01 to 3.09E+01 and-3.05E+01 to 1.79E+03 mg m-2day-1, respectively. In the case ofthe activated sludge units (i.e. reactor, secondary settler and sludge storage tank) emission rates CO2, N2O and CH4 ranged from 1.56E+04 to 1.43E+05, 1.13E+01 to 4.75E+01 and 2.52E+01 to 1.01E+03 mg m-2 day-1, respectively. Seasonally, daily and instantaneous variability in emissions as well as spatial variability was found. Comparing CWs with the activated sludge system, surface emission rates were lower in the CWs system in both seasons considered. Results highlighted that CWs are suitable technologies that can help to reduce GHG emis-sions associated with winery wastewater treatment. This research was funded by the European Regional Development Fund (Interreg V-B SUDOE programme, WETWINE SOE1/P5/E0300) (http://wetwine.eu/). Laura Flores is grateful to the Ministry of Education and Culture (MECD) (Spain) for the FPU16/01491 scholarship. Joan García, Marianna Garfí and Laura Flores are grateful to the Government of Catalonia (Consolidated Research Group 2017 SGR 1029). Marianna Garfí is grateful to the Spanish Ministry of Economy and Competitiveness (RYC-2016-20059). The authors are grateful for the support provided by the WETWINE consortium and the wineries involved in this study. The results shown in this paper were presented in the 2019 WETPOL Conference (Aarhus, Denmark) under the oral presentation: “Constructed wetlands release less greenhouse gas emissions than activated sludge: a key point for their implementation in the wine sector” by Laura Flores who was awarded with the First Prize in the Society of Wetland Scientists (SWS) Europe Chapter Oral Presentation Competition. Peer Reviewed Objectius de Desenvolupament Sostenible::6 - Aigua Neta i Sanejament Objectius de Desenvolupament Sostenible::6 - Aigua Neta i Sanejament::6.3 - Per a 2030, millorar la qualitat de l’aigua mitjançant la reducció de la contaminació, l’eliminació dels abocaments i la reducció al mínim de la descàrrega de materials i productes químics perillosos, la reducció a la meitat del percentatge d’aigües residuals sense tractar, i un augment substancial a escala mundial del reciclat i de la reutilització en condicions de seguretat

Lichens contribute significantly to the biodiversity and functioning of many ecosystems. Although lichens are useful air pollution bioindicators and may respond in significant ways to global change, they are studied infrequently under field conditions in chamberless exposure systems. We surveyed corticolous lichens on paper birch (Betula papyrifera) and trembling aspen (Populus tremuloides) after 10 years exposure (1998-2007) to elevated CO

The ocean acidification may severely affect macroalgal communities of the shallowest coastal habitats where they play relevant structural and functional roles. In this paper were investigated the physiological traits of two populations of Dictyota dichotoma var. intricata, living at two different pH for several generations to assess the reasons of the algae reduced abundance at current (8.1) compared to low pH (6.7). Besides, through transplant experiments, the two populations were analysed for the stress response and reversibility of physiological performance at different pH. The long-term acclimation to high pCO

Nowadays, climate change is one of the most important global issues to the international community. And nearly thirty kinds of greenhouse gases have been found in the atmosphere, of which the carbon dioxide plays a crucial role. In this paper, the carbon dioxide emissions of BRICS (Brazil, Russia, India, China and South Africa) countries are investigated by using a conformable fractional non-homogeneous grey model. The grey model is systematically studied based on the new definitions of the conformable fractional accumulation and difference. The closed-form solutions of the new model are derived by applying mathematical tools and grey theory. And the meta-heuristic algorithm ant lion optimizer is adopted to search optimal fractional order. With raw data during the period from 2000 to 2018 announced by British Petroleum, the new model is established to forecast the carbon dioxide emissions of BRICS nations from 2019 to 2025. The results show that the trend of the carbon dioxide emissions of Brazil and India is growing year by year, the pattern of Russia is fluctuant but remains stable generally, while China and South Africa reach its peak value in 2019, and then decrease in the next several years. It also demonstrates that the governments of Brazil and India should take more measures to reduce carbon dioxide emissions, while the governments of China and South Africa should keep up their crucial work on carbon dioxide emissions.

Before the industrial revolution, the atmospheric CO2 concentration was 180–330 ppm; however, fossil-fuel combustion and forest destruction have led to increased atmospheric CO2 concentration. CO2 capture and storage is regarded as a promising strategy to prevent global warming and ocean acidification and to alleviate elevated atmospheric CO2 concentration, but the leakage of CO2 from storage system can lead to rapid acidification of the surrounding circumstance, which might cause negative influence on environmental microbes. The effects of elevated CO2 on microbes have been reported extensively, but the review regarding CO2 affecting different environmental microorganisms has never been done previously. Also, the mechanisms of CO2 affecting environmental microorganisms are usually contributed to the change of pH values, while the direct influences of CO2 on microorganisms were often neglected. This paper aimed to provide a systematic review of elevated CO2 affecting environmental microbes and its mechanisms. Firstly, the influences of elevated CO2 and potential leakage of CO2 from storage sites on community structures and diversity of different surrounding environmental microbes were assessed and compared. Secondly, the adverse impacts of CO2 on microbial growth, cell morphology and membranes, bacterial spores, and microbial metabolism were introduced. Then, based on biochemical principles and knowledge of microbiology and molecular biology, the fundamental mechanisms of the influences of carbon dioxide on environmental microbes were discussed from the aspects of enzyme activity, electron generation and transfer, and key gene and protein expressions. Finally, key questions relevant to the environmental effect of CO2 that need to be answered in the future were addressed.

China has proposed the Belt and Road Initiative to promote the cooperation of energy production and trade between the relevant countries. This paper investigates the relationship between the energy consumption and economic growth of the countries along the Belt and Road using a panel of data for 69 countries during the period between 1970 and 2013. Both of the renewable and traditional fossil energy consumptions are investigated in this study. By employing vector error correction model (VECM), fully modified OLS (FMOLS) and dynamic OLS (DOLS) approaches, the estimation results indicate that the nexuses of the energy consumption and economic developments vary across different subgroups. For the entire group, there is evidence of long-run bidirectional causalities among carbon emissions, energy use, industry value added and GDP per capita. For the energy-importing countries along the Belt and Road, there exists unidirectional short-run causality running from GDP to renewable energy and long-run causality in the reverse direction. In contrast, for the energy-exporting countries, there is a bidirectional causality between the energy use and GDP per capita in the long run. These findings suggest significant cooperation potential in the economies and trades of China and the Belt and Road countries.

This study predicts the probabilities of achieving the carbon dioxide (CO2) emission targets set by the Paris Agreement and the Intended Nationally Determined Contribution (INDC) of the top ten CO2 emitters (TTCE). The TTCE are China, USA, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia and Indonesia based on their emission trends over 1991–2015 period. The methods of trend extrapolation and back propagation (BP) neural networks are used in this paper to overcome the weakness of multiple linear regression (MLR) and the assumptions of the environmental Kuznets curve (EKC). The results show that the model performs well and has high predictive accuracy. The volume of the CO2 emissions by the TTCE in 2030 is predicted to increase by 26.5–36.5%, compared with 2005. According to different trends of economic growth, energy consumption, and changes in share of renewable energy, the results show that China, India and Russia will achieve their INDC targets in some scenarios, whereas there will be a shortfall in achieving targets by USA, Japan, Germany, and South Korea. In particular, the carbon reduction situations of Saudi Arabia, Iran and Indonesia are quite severe. Moreover, the results show that there is no common trend that can be used as a suitable benchmark for every country for the implementation of carbon reductions targets of the Paris Agreement and their INDC goals. Finally, there are signs of improvement of the equality of carbon emissions based on the analysis of the Gini coefficient.

The transport sector is one of the main sources of greenhouse gases, adding in Spain near a quarter of the total national emissions, the majority in road transport. Therefore, road contribution to climate change should be mitigated to achieve the proposed goals in the fight against climate change. Policies and strategies suggest several preventive mitigation options, but have paid little attention to compensatory mitigation. We have conducted a theoretical case study in a Spanish province, Segovia, estimating the carbon dioxide emissions in the road network between 2015 and 2050, and analysing different compensation possibilities through conservation agriculture, agroforestry, afforestation and hedgerow plantation. We have calculated carbon sequestration in the reference period and costs per tonne for each option, estimating the budget range of offsetting road carbon emission, and funding possibilities, especially through fuel taxes. The paper demonstrates that offsetting carbon emissions produced by roads in this area is technically possible and highly desirable, unifying carbon sequestration, biodiversity improvement and rural development. The main challenge is funding, which depends largely on the political will and the awareness of the citizens.

The greenhouse gases and black carbon emission inventory from IPCC key category Energy was accomplished for the Mezquital Valley, one of the most polluted regions in Mexico, as the Mexico City wastewater have been continuously used in agricultural irrigation for more than a hundred years. In addition, thermoelectric, refinery, cement and chemistry industries are concentrated in the southern part of the valley, near Mexico City. Several studies have reported air, soil, and water pollution data and its main sources for the region. Paradoxically, these sources contaminate the valley, but boosted its economic development. Nevertheless, no research has been done concerning GHG emissions, or climate change assessment. This paper reports inventories performed by the 1996 IPCC methodology for the baseline year 2005. Fuel consumption data were derived from priority sectors such as electricity generation, refineries, manufacturing & cement industries, transportation, and residential use. The total CO2 emission result was 13,894.9 Gg, which constituted three-quarters of Hidalgo statewide energy category. The principal CO2 sources were energy transformation (69%) and manufacturing (19%). Total black carbon emissions were estimated by a bottom-up method at 0.66 Gg. The principal contributor was on-road transportation (37%), followed by firewood residential consumption (26%) and cocked brick manufactures (22%). Non-CO2 gas emissions were also significant, particularly SO2 (255.9 Gg), which accounts for 80% of the whole Hidalgo State emissions. Results demonstrated the negative environmental impact on Mezquital Valley, caused by its role as a Megacity secondary fuel and electricity provider, as well as by the presence of several cement industries.

The Kyoto protocol has established an accounting system for national greenhouse gas (GHG) emissions according to a geographic criterion ( producer perspective ), such as that proposed by the IPCC guidelines for national GHG inventories. However, the representativeness of this approach is still being debated, because the role of final consumers ( consumer perspective ) is not considered in the emission allocation system. This paper explores the usefulness of a hybrid analysis, including input–output (IO) and process inventory data, as a complementary tool for estimating and allocating national GHG emissions according to both consumer - and producer -based perspectives. We assess the historical GHG impact profile (from 1995 to 2009) of Luxembourg, which is taken as a case study. The country's net consumption over time is estimated to generate about 28,700 Gg CO 2 e/year on average. Compared to the conventional IPCC inventory, the IO-based framework typically shows much higher emission estimations. This relevant discrepancy is mainly due to the different points of view obtained from the hybrid model, in particular with regard to the contribution of imported goods and services. Detailing the GHG inventory by economic activity and considering a wider system boundary make the hybrid IO method advantageous as compared to the IPCC approach, but its effective implementation is still limited by the relatively complex modeling system, as well as the lack of coordination and scarce availability of datasets at the national level.

This paper provides evidence that symmetry of CO 2 concentration distribution may indicate sources or dispersive processes. Skewness was calculated by different procedures with CO 2 measured at a rural site using a Picarro G1301 analyser over a two-year period. The usual skewness coefficient was considered together with fourteen robust estimators. A noticeable contrast was obtained between day and night, and skewness decreased linearly with the logarithm of the height. One coefficient was selected from its satisfactory relationship with the median concentration in daily evolution. Three analyses based on the kernel smoothing method were conducted with this coefficient to investigate its response to yearly and daily evolutions, wind direction, and wind speed. Left-skewed distributions were linked to thermal turbulence during midday, especially in spring–summer, or with high wind speeds. Almost symmetric distributions were associated with sources, such as the Valladolid City plume reinforced with spring emissions and the lack of emissions in summer in the remaining directions. Finally, right-skewed distributions were related to low wind speeds and stable stratification at night, furthered by strong emissions in spring. Skewness intervals were proposed and their average median concentrations were calculated such that the relationship between skewness and concentration depends on the analysis performed. Since some skewness coefficients may also be negative, they provide better information about sources or dispersive processes than concentration.

In this paper, we present the first dispersive infrared spectroscopic (DIRS) measurement of atmospheric carbon dioxide (CO2) using a new scanning Fabry-Pérot interferometer (FPI) sensor. The sensor measures the optical spectra in the mid infrared (3,900 nm to 5,220 nm) wavelength range with full width half maximum (FWHM) spectral resolution of 78.8 nm at the CO2 absorption band (~4,280 nm) and sampling resolution of 20 nm. The CO2 concentration is determined from the measured optical absorption spectra by fitting it to the CO2 reference spectrum. Interference from other major absorbers in the same wavelength range, e.g., carbon monoxide (CO) and water vapor (H2O), was taken out by including their reference spectra in the fit as well. The detailed descriptions of the instrumental setup, the retrieval procedure, a modeling study for error analysis as well as laboratory validation using standard gas concentrations are presented. An iterative algorithm to account for the non-linear response of the fit function to the absorption cross sections due to the broad instrument function was developed and tested. A modeling study of the retrieval algorithm showed that errors due to instrument noise can be considerably reduced by using the dispersive spectral information in the retrieval. The mean measurement error of the prototype DIRS CO2 measurement for 1 minute averaged data is about ±2.5 ppmv, and down to ± 0.8ppmv for 10 minute averaged data. A field test of atmospheric CO2 measurements were carried out in an urban site in Hong Kong for a month and compared to a commercial non-dispersive infrared (NDIR) CO2 analyzer. 10 minute averaged data shows good agreement between the DIRS and NDIR measurements with Pearson correlation coefficient (R) of 0.99. This new method offers an alternative approach of atmospheric CO2 measurement featuring high accuracy, correction of non-linear absorption and interference of water vapor.

This paper presents the results of a field investigation into the effect of rainfall on the diurnal variations of CH(4), CO(2), and N(2)O fluxes at three different sites in a landfill. The three sites were all net sources of CH(4), CO(2), and N(2)O emissions ranging from 4.7-796 mg CH(4)-Cm(-2)h(-1), 131-811 mg CO(2)-Cm(-2)h(-1), and 90.6-2,209 μg N(2)O-Nm(-2)h(-1). CH(4) and N(2)O were generated by anaerobic digestion of waste and by nitrification and denitrification in the cover soil, respectively. Installation of an HDPE (High Density Polyethylene) membrane cover at landfill Site 1 was very effective in controlling CH(4) emissions, and the CH(4) fluxes at that site were 40-50 times lower than those at Site 2 and Site 3. For 36 h following a rainfall event, both CH(4) and N(2)O emissions were significantly correlated with moisture content of the cover soil at the two landfill sites without HDPE membrane cover (p0.05). When CH(4) and N(2)O fluxes were expressed as CO(2)-equivalent (CO(2)-e) emissions, CH(4) emissions dominated in the three gases after rainfall (96.1% CO(2)-e emissions) and could be expressed by the following equation: Y(CH4)=988.7+41.1X((soil moisture content))-32.2X((soil temperature)) (R(2)=0.73, p0.05).

To mitigate anthropogenic climate change greenhouse gas emissions (GHG) must be reduced; their major source is man's use of energy. A key way to manage emissions is for the energy consumer to understand their impact and the consequences of changing their activities. This paper addresses the challenge of delivering relevant, practical and reliable greenhouse gas 'footprint' information for small and medium sized businesses. The tool we describe is capable of ascribing parts of the total footprint to specific actions to which the business can relate and is sensitive enough to reflect the consequences of change. It provides a comprehensive description of all emissions for each business and sets them in the context of local, national and global statistics. It includes the GHG costs of all goods and services irrespective of their origin and without double accounting. We describe the development and use of the tool, which draws upon both national input-output data and process-based life cycle analysis techniques; a hybrid model. The use of national data sets the output in context and makes the results consistent with national and global targets, while the life cycle techniques provide a means of reflecting the dynamics of actions. The model is described in some detail along with a rationale and a short discussion of validity. As the tool is designed for small commercial users, we have taken care to combine rigour with practicality; parameterising from readily available client data whilst being clear about uncertainties. As an additional incentive, we also report on the potential costs or savings of switching activities. For users to benefit from the tool, they need to understand the output and know how much confidence they should place in the results. We not only describe an application of non-parametric statistics to generate confidence intervals, but also offer users the option of and guidance on adjusting figures to examine the sensitivity of the model to its components. It is important that the user does not see the model as a calculator that will generate one truth, but as a method of gaining insight and informing management decisions. We describe its application in tourism businesses in North West England as a demonstrator for the service sector remote from simple primary production, with brief case studies. We discuss its success compared to traditional approaches and outline further development work.

Exhaust emissions from thirteen compressed natural gas (CNG) and nine ultralow sulphur diesel in-service transport buses were monitored on a chassis dynamometer. Measurements were carried out at idle and at three steady engine loads of 25%, 50% and 100% of maximum power at a fixed speed of 60 km h(-1). Emission factors were estimated for particle mass and number, carbon dioxide and oxides of nitrogen for two types of CNG buses (Scania and MAN, compatible with Euro 2 and 3 emission standards, respectively) and two types of diesel buses (Volvo Pre-Euro/Euro1 and Mercedez OC500 Euro3). All emission factors increased with load. The median particle mass emission factor for the CNG buses was less than 1% of that from the diesel buses at all loads. However, the particle number emission factors did not show a statistically significant difference between buses operating on the two types of fuel. In this paper, for the very first time, particle number emission factors are presented at four steady state engine loads for CNG buses. Median values ranged from the order of 10(12) particles min(-)(1) at idle to 10(15) particles km(-)(1) at full power. Most of the particles observed in the CNG emissions were in the nanoparticle size range and likely to be composed of volatile organic compounds The CO2 emission factors were about 20% to 30% greater for the diesel buses over the CNG buses, while the oxides of nitrogen emission factors did not show any difference due to the large variation between buses.

An approach that incorporates three modelling components has been developed to estimate road traffic CO2 emissions for an urban area with street level resolution. The first component enables the determination of the road traffic characteristics using the SATURN (Simulation and Assignment of Traffic in Urban Road Networks) model. The output from this component is then analysed using MATrix LABoratory (MATLAB) programming to provide estimates of CO2 emissions for the urban area. Finally, ArcGIS is used to illustrate the model output. The three components are integrated using a Loose-Coupling approach in which the individual components each load the necessary data to give an independent output. The model structure is discussed in the current paper and the modelling results for a small city (Norwich, UK) are presented.