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As China's second-largest source of greenhouse gas emissions, agriculture is essential to achieving the goal of "carbon peak" and "carbon neutrality." Based on the measurement of agricultural carbon emissions (ACE) and agricultural carbon intensity (ACI) in 19 regions along the Yangtze River Economic Belt (YEB) and Yellow River Basin (YRB) in China from 2001 to 2020, this paper first uses the super-efficiency SBM model to measure ACE efficiency from static and dynamic perspectives. Then, the coupling coordination degree (CCD) between ACE efficiency and green finance in each region of the two basins is explored. Finally, Grey Relation Analysis (GRA) is used to obtain the influencing factors of CCD. The following conclusions are drawn: (1) The ACE in the YEB is almost twice that of the YRB. The ACE of the two basins generally experienced a trend of first growth and then declined, but the peak time was different. The ACI of the two basins showed a trend of continuous decline, and the decline rate of the YRB was faster. (2) The ACE efficiency of the two basins showed an overall upward trend, and the growth degree of different regions was vastly different. From the factor decomposition, the technological progress (TP) of the two basins significantly impacts the total factor productivity (TFP). (3) The CCD of ACE efficiency and green finance in the two basins increased from near imbalance to barely coordination level, and the CCD of the YEB increased slightly faster. The CCD of the two basins has a spatial difference of "downstream > midstream > upstream." (4) Among the influencing factors of the CCD of the two systems, the influencing degree of the factors is as follows from large to small: quality of human capital, level of economic development, government regulation, scientific and technological innovation ability. [ABSTRACT FROM AUTHOR]

Introduction: The current food system is associated with negative impacts on health, food insecurity and environmental harm. Sustainable diets have attracted increasing interest and novel proposals with a global scope have emerged. This scoping review aims to give an overview of the analysis of all the available evidence related to the sustainable diet indices that have been developed based on the EAT-Lancet Commission. Methods: Searches were conducted in the PubMed, Embase, Web of Science, Scopus and Science Direct databases. This review was conducted following the PRISMA-ScR guidelines. The target population were studies addressed the use of an index or metric for assessing sustainable diets based on the EAT-Lancet Commission Summary Report were included. PCC acronym was used in the design of the study to describe eligibility criteria: P (Population)—Indexes; C (Concept)—Sustainable diets; C (Context)—Knowledge on the structure and applicability of measurement indices of sustainable diets based on EAT-Lancet recommendations available in the literature. Study eligibility criteria were restricted to papers published in English, from January 2019 through October 2022, with no population restriction. Results: A total of 1,458 papers were retrieved, 14 of which were included in the review. Seven measures of sustainable diets were identified as follow: EAT-Lancet diet score (ELD-I), New EAT-Lancet diet score (EAT), Planetary Health Diet Index (PHDI), Sustainable Diet Index (SDI), Sustainable-HEalthy-Diet (SHED), novel Nutrient-Based EAT index (NB-EAT) and World Index for Sustainability and Health (WISH). Most studies were conducted in developed countries, where greater adherence to this type of diet was found. Estimated greenhouse gas emissions was the most reported indicator of sustainability, followed by diet quality and the benefits of sustainable diets with regards to health outcomes. Discussion: We identified barriers that hinder progress towards sustainable diets, including the difficulty of comparing different indices and the tendency to neglect social aspects and the lack of common definitions and metrics. Despite being challenge, we highlight the importance of using indices that assess sustainable diets that harmonize various indicators, as recommended by the EAT-Lancet Commission, in order to promote positive changes towards a more sustainable future. [ABSTRACT FROM AUTHOR]

Rapid uptake of greenhouse gas (GHG) mitigation measures is central to reducing agricultural and land use emissions and meeting the UK Net Zero policy. The socioeconomic challenges and barriers to uptake are poorly understood, with yet unclear structural pathways to the uptake of GHG mitigation measures. Using an online survey of 201 agricultural land managers across the UK, and applying multiple linear regression and stepwise regression analysis, this research established farm and farmers' factors influencing perceptions and willingness to adopt GHG mitigation measures. The results consistently show that farm sector, farmers' business perception, and labour availability influence willingness to adopt GHG mitigation measures. Based on the farmers' qualitative feedback, other barriers to adoption include costs and concerns for profitability, lack of flexibility in land tenancy contracts, poor awareness and knowledge of the application of some GHG mitigation measures, perception about market demand e.g bioenergy crops, and scepticism about the future impacts of adopting varying GHG mitigation measures. In the midst of the ongoing net zero transition, this study identifies existing barriers to the uptake of GHG mitigation measures, and specifically, a substantial gap between farmers and the science of GHG mitigation measures and the need to incentivise a farm and farming community-led policy interventions to promote adoption of GHG mitigation measures., Competing Interests: The authors declare no known competing interests that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

At present, renewable energy sources (RESs) and electric vehicles (EVs) are presented as viable solutions to reduce operation costs and lessen the negative environmental effects of microgrids (μGs). Thus, the rising demand for EV charging and storage systems coupled with the growing penetration of various RESs has generated new obstacles to the efficient operation and administration of these μGs. In this regard, this paper introduces a multi-objective optimization model for minimizing the total operation cost of the μG and its emissions, considering the effect of battery storage system (BSS) and EV charging station load. A day-ahead scheduling model is proposed for optimal energy management (EM) of the μG investigated, which comprises photovoltaics (PVs), fuel cells (FCs), wind turbines (WTs), BSSs, and EV charging stations, with shed light on the viability and benefits of connecting BSS with EV charging stations in the μG. Analyzing three case studies depending on the objective function—Case 1: execute EM to minimize total operation cost and maximize the profits of BSS, Case 2: execute EM to minimize total emission from the μG, and Case 3: execute EM to minimize total operation cost, maximize the profits of BSS, and minimize total emissions from the μG. The main aim of the presented optimization strategy is to achieve the best possible balance between reducing expenses and lessening the environmental impact of greenhouse gas emissions. The krill herd algorithm (KHA) is used to find the optimal solutions while considering various nonlinear constraints. To demonstrate the validity and effectiveness of the proposed solution, the study utilizes the KHA and compares the obtained results with those achieved by other optimization methods. It was demonstrated that such integration significantly enhances the μG's operational efficiency, reduces operating costs, and minimizes environmental impact. The findings underscore the viability of combining EV charging infrastructure with renewable energy to meet the increasing energy demand sustainably. The novelty of this work lies in its multi-objective optimization approach, the integration of EV charging and BSS in μGs, the comparison with other optimization methods, and the emphasis on sustainability and addressing energy demand through the utilization of renewable energy and EVs. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions, as one of the primary contributors to global warming, present an urgent environmental challenge that requires attention. Accurate prediction of carbon dioxide (CO2) emissions from the industrial sector is crucial for the development of low-carbon industries. However, existing time series models often suffer from severe overfitting when data volume is insufficient. In this paper, we propose a carbon emission prediction method based on meta-learning and differential long- and short-term memory (MDL) to address this issue. Specifically, MDL leverages Long Short-Term Memory (LSTM) to capture long-term dependencies in time series data and employs a meta-learning framework to transfer knowledge from multiple source task datasets for initializing the carbon emission prediction model for the target task. Additionally, the combination of differential LSTM and the meta-learning framework reduces the dependency of the differential long- and short-term memory network on data volume. The smoothed difference method, included in this approach, mitigates the randomness of carbon emission sequences, consequently benefiting the fit of the LSTM model to the data. To evaluate the effectiveness of our proposed method, we validate it using carbon emission datasets from 30 provinces in China and the industrial sector in Xinjiang. The results show that the average absolute error (MAE), Coefficient of Determination (R2) and root mean square error (RMSE) of the method have been reduced by 61.8% and 63.8% on average compared with the current mainstream algorithms. The method provides an efficient and accurate solution to the task of industrial carbon emission prediction, and helps environmental policy makers to formulate environmental policies and energy consumption plans. [ABSTRACT FROM AUTHOR]

This paper aims to effectively reduce CO2 emissions by examining the impact of three distinct incentive and constraint policies on the quality of rating and certification information in China's green bond issuance market. To accomplish this, the government has implemented incentives, while regulators have introduced constraints to curb the spread of inflated rating and certification information. We build on the integrated rating and certification regulation mechanism by presenting a two-stage Stackelberg game model that involves four key participants: the China Securities Regulatory Commission, local governments, green evaluation and certification agencies, and credit rating agencies. We incorporate environmental effects indicators into the expected utility of rating and certification agencies to investigate the equilibrium conditions under three policy scenarios: a single financial incentive policy, a single regulatory constraint policy, and a combined incentive and constraint policy. The paper employs Stackelberg game theory to analyze how different policies mitigate the occurrence of "inflated" ratings and "greenwashing" in certifications. Numerical analysis is conducted to validate the theoretical findings. Moreover, we assess the impact of these policies on the quality of rating and evaluation information, using data from China's green bond issuance market between 2016 and 2021. Our research offers valuable management insights and regulatory recommendations for both regulators and local governments. [ABSTRACT FROM AUTHOR]

Based on the integrated model of Super-SBM model, spatial Durbin model (SDM) and Grey neural network model, this paper analyzes the panel data of various provinces in China from multiple angles and dimensions. It was found that there were significant differences in eco-efficiency between organic rice production and conventional rice production. The response of organic rice to climate change, the spatial distribution of ecological and economic benefits and the impact on carbon emission were analyzed. The results showed that organic rice planting not only had higher economic benefits, but also showed a rising trend of ecological benefits and a positive feedback effect. This finding highlights the importance of organic rice farming in reducing carbon emissions. Organic rice farming effectively reduces greenhouse gas emissions, especially carbon dioxide and methane, by improving soil management and reducing the use of fertilizers and pesticides. This has important implications for mitigating climate change and promoting soil health and biodiversity. With the acceleration of urbanization, the increase of organic rice planting area shows the trend of organic rice gradually replacing traditional rice cultivation, further highlighting the potential of organic agriculture in emission reduction, environmental protection and sustainable agricultural production. To this end, it is recommended that the Government implement a diversified support strategy to encourage technological innovation, provide guidance and training, and raise public awareness and demand for organic products. At the same time, private sector participation is stimulated to support the development of organic rice cultivation through a public-private partnership model. Through these measures, further promote organic rice cultivation, achieve the dual goals of economic benefits and environmental benefits, and effectively promote the realization of double carbon emission reduction targets., Competing Interests: This work received the following funding: National Social Science Fund Project, 18XMZ041., (Copyright: © 2024 Luo, Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Agrifood systems account for 31% of global greenhouse gas emissions. Substantial emissions reduction in agrifood systems is critical to achieving the temperature goal set by the Paris Agreement. A key challenge in reducing GHG emissions in the agrifood value chain is the imbalanced allocation of benefits and costs associated with emissions reduction among agrifood value chain participants. However, only a few studies have examined agrifood emissions reduction from a value chain perspective, especially using dynamic methods to investigate participants' long-term emissions reduction strategies. This paper helps fill this gap in the existing literature by examining the impact of collaborations among agrifood value chain participants on correcting those misallocations and reducing emissions in agrifood systems. We develop a dynamic differential game model to examine participants' long-term emissions reduction strategies in a three-stage agrifood value chain. We use the Hamilton-Jacobi-Bellman equation to derive the Nash equilibrium emissions reduction strategies under non-cooperative, cost-sharing, and cooperative mechanisms. We then conduct numerical analysis and sensitivity analysis to validate our model. Our results show that collaboration among value chain participants leads to higher emissions reduction efforts and profits for the entire value chain. Specifically, based on our numerical results, the cooperative mechanism results in the greatest emissions reduction effort by the three participants, which leads to a total that is nearly three times higher than that of the non-cooperative mechanism and close to two times higher than the cost-sharing mechanism. The cooperative mechanism also recorded the highest profits for the entire value chain, surpassing the non-cooperative and cost-sharing mechanisms by around 37% and 16%, respectively. Our results provide valuable insights for policymakers and agrifood industry stakeholders to develop strategies and policies encouraging emissions reduction collaborations in the agrifood value chain and reduce emissions in the agrifood systems., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Organic soil amendments are used to improve soil quality and mitigate climate change. However, their effects on soil structure, nutrient and water retention as well as greenhouse gas (GHG) emissions are still poorly understood. The purpose of this study was to determine the residual effects of a single field application of four ligneous soil amendments on soil structure and GHG emissions. We conducted a laboratory incubation experiment using soil samples collected from an ongoing soil-amendment field experiment at Qvidja Farm in south-west Finland, two years after a single application of four ligneous biomasses. Specifically, two biochars (willow and spruce) produced via slow pyrolysis, and two mixed pulp sludges from paper industry side-streams were applied at a rate of 9-22 Mg ha-1 mixed in the top 0.1 m soil layer. An unamended fertilized soil was used as a control. The laboratory incubation lasted for 33 days, during which the samples were kept at room temperature (21°C) and at 20%, 40%, 70% or 100% water holding capacity. Carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) fluxes were measured periodically after 1, 5, 12, 20 and 33 days of incubation. The application of ligneous soil amendments increased the pH of the sampled soils by 0.4-0.8 units, whereas the effects on soil organic carbon content and soil structure varied between treatments. The GHG exchange was dominated by CO2 emissions, which were mainly unaffected by the soil amendment treatments. The contribution of soil CH4 exchange was negligible (nearly no emissions) compared to soil CO2 and N2O emissions. The soil N2O emissions exhibited a positive exponential relationship with soil moisture. Overall, the soil amendments reduced N2O emissions on average by 13%, 64%, 28%, and 37%, at the four soil moisture levels, respectively. Furthermore, the variation in N2O emissions between the amendments correlated positively with their liming effect. More specifically, the potential for the pulp sludge treatments to modulate N2O emissions was evident only in response to high water contents. This tendency to modulate N2O emissions was attributed to their capacity to increase soil pH and influence soil processes by persisting in the soil long after their application., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Peltokangas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In the past 40 years, the continuous strengthening of the greenhouse effect has led to a significant increase in the global average temperature. Although people's understanding of climate change has been strengthened, the world has not yet witnessed a significant decline in pollutant emissions; hence it is imperative to get to the root cause. This paper is based on the STIRPAT model framework and uses the panel data of G20 countries over the period 1999–2019 to examine the role of financial development on carbon emissions under good governance. The results show that financial development significantly promotes carbon dioxide emissions, and the impact presents an inverted "U"-shaped trend when the quadratic term of financial development is introduced. Surprisingly, governance quality indicators increase carbon emissions. However, financial development accompanied by good governance suppresses carbon emissions. Moreover, according to the grouped results of developed and developing countries, different nations should adopt differentiated strategies in development finance to implement the carbon emission targets proposed by the G20. In addition, this paper also confirms the existence of the Environmental Kuznets Curve hypothesis. In light of this, policymakers should optimize the quality of governance while shifting their agendas toward environmentally responsible financial practices to promote financial development to improve environmental quality effectively. Furthermore, strengthen international cooperation, enhance public environmental protection concepts, and take joint actions to achieve low-carbon and win-win results. [ABSTRACT FROM AUTHOR]

Background and aim: Colonoscopy is the gold-standard screening test for colorectal cancer. However, it has come under scrutiny for its carbon footprint and contribution to greenhouse gas (GHG) emissions compared to other medical procedures. Notwithstanding, screening colonoscopies may have a positive effect on GHG emissions that is unknown. This study estimated the carbon emissions prevented by screening colonoscopies in the U.S. Methods: Using the reported number of screening colonoscopies performed annually in the U.S. and the absolute risk reduction (ARR) reported in the NorDICC trial, we calculated the expected minimum number of cancer treatment and surveillance visits prevented through screening based on the cancer stage. The average carbon emission averted per mile traveled was computed using the Environmental Protection Agency's (EPA) GHG equivalencies calculator. The final estimate of carbon emissions averted over a decade by screening colonoscopies performed in one year was determined. Result: 6.3 million screening colonoscopies performed in one year prevent 1,134,000 colorectal cancers over a ten-year period. Of these, 38∙3% (434,254) are localized, 38∙8% (440,281) are regional, and 22∙9% (259,465) are metastatic disease. The minimum number of post-diagnosis visits prevented is 11 for stage I, ≥ 21 for stage II, ≥25 for stage III, and ≥ 20 for stage IV disease, comprised of diagnostic, surgical evaluation, chemotherapy, and surveillance visits. The total number of visits prevented by screening is 2,388,397 for stage I, 5,254,421 for stage II, 13,120,369 for stage III, and 9,210,972 for stage IV disease. Approximately 395 million miles of travel and 158,263 metric tons of CO2, equivalent to 177 million pounds of coal burned, 19 billion smartphones charged, or 18 million gallons of gasoline consumed, were saved over ten years through screening. Conclusion: Colorectal cancer screening decreases cancer-related GHG emissions and minimizes the environmental impact of cancer treatment. [ABSTRACT FROM AUTHOR]

Global warming, caused by greenhouse gas emissions, is a major challenge for all human societies. To ensure that ambitious carbon neutrality and sustainable economic development goals are met, regional human activities and their impacts on carbon emissions must be studied. Guizhou Province is a typical karst area in China that predominantly uses fossil fuels. In this study, a backpropagation (BP) neural network and extreme learning machine (ELM) model, which is advantageous due to its nonlinear processing, were used to predict carbon emissions from 2020 to 2040 in Guizhou Province. The carbon emissions were calculated using conversion and inventory compilation methods with energy consumption data and the results showed an "S" growth trend. Twelve influencing factors were selected, however, five with larger correlations were screened out using a grey correlation analysis method. A prediction model for carbon emissions from Guizhou Province was established. The prediction performance of a whale optimization algorithm (WOA)-ELM model was found to be higher than the BP neural network and ELM models. Baseline, high-speed, and low-carbon scenarios were analyzed and the size and time of peak carbon emissions in Liaoning Province from 2020 to 2040 were predicted using the WOA-ELM model. [ABSTRACT FROM AUTHOR]

Background: Health care has significant environmental impact. We performed a scoping review to map what is known about the environmental impact of health care for musculoskeletal conditions., Methods: We included published papers of any design that measured or discussed environmental impact of health care or health support services for any musculoskeletal condition in terms of climate change or global warming (e.g., greenhouse gas emissions it produces). We searched MEDLINE and Embase from inception to 2 May 2022 using keywords for environmental health and musculoskeletal conditions, and performed keyword searches using Google and Google Scholar. Two independent reviewers screened studies. One author independently charted data, verified by a second author. A narrative synthesis was performed., Results: Of 12,302 publications screened and 73 identified from other searches, 122 full-text articles were assessed for eligibility, and 49 were included (published 1994 to 2022). Of 24 original research studies, 11 measured environmental impact relating to climate change in orthopaedics (n = 10), and medical aids for the knee (n = 1), one measured energy expenditure of laminar versus turbulent airflow ventilation systems in operating rooms during simulated hip replacements and 12 measured waste associated with orthopaedic surgery but did not relate waste to greenhouse gas emissions or environmental effects. Twenty-one editorials described a need to reduce environmental impact of orthopaedic surgery (n = 9), physiotherapy (n = 9), podiatry (n = 2) or occupational therapy (n = 1). Four narrative reviews discussed sustainability relating to hand surgery (n = 2), orthopaedic surgery (n = 1) and orthopaedic implants (n = 1)., Conclusion: Despite an established link between health care and greenhouse gas emissions we found limited empirical data estimating the impact of musculoskeletal health care on the environment. These data are needed to determine whether actions to lower the carbon footprint of musculoskeletal health care should be a priority and to identify those aspects of care that should be prioritised., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 McKenzie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Agriculture in India accounts for 18% of greenhouse gas (GHG) emissions and uses significant land and water. Various socioeconomic factors and food subsidies influence diets in India. Indian food systems face the challenge of sustainably nourishing the 1.3 billion population. However, existing studies focus on a few food system components, and holistic analysis is still missing. We identify Indian food systems covering six food system components: food consumption, production, processing, policy, environmental footprints, and socioeconomic factors from the latest Indian household consumer expenditure survey. We identify 10 Indian food systems using k-means cluster analysis on 15 food system indicators belonging to the six components. Based on the major source of calorie intake, we classify the ten food systems into production-based (3), subsidy-based (3), and market-based (4) food systems. Home-produced and subsidized food contribute up to 2000 kcal/consumer unit (CU)/day and 1651 kcal/CU/day, respectively, in these food systems. The calorie intake of 2158 to 3530 kcal/CU/day in the food systems reveals issues of malnutrition in India. Environmental footprints are commensurate with calorie intake in the food systems. Embodied GHG, land footprint, and water footprint estimates range from 1.30 to 2.19 kg CO2eq/CU/day, 3.89 to 6.04 m2/CU/day, and 2.02 to 3.16 m3/CU/day, respectively. Our study provides a holistic understanding of Indian food systems for targeted nutritional interventions on household malnutrition in India while also protecting planetary health., Competing Interests: The authors declare no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Global biodiversity is negatively affected by anthropogenic climate change. As species distributions shift due to increasing temperatures and precipitation fluctuations, many species face the risk of extinction. In this study, we explore the expected trend for plant species distributions in Central America and southern Mexico under two alternative Representative Concentration Pathways (RCPs) portraying moderate (RCP4.5) and severe (RCP8.5) increases in greenhouse gas emissions, combined with two species dispersal assumptions (limited and unlimited), for the 2061–2080 climate forecast. Using an ensemble approach employing three techniques to generate species distribution models, we classified 1924 plant species from the region's (sub)tropical forests according to IUCN Red List categories. To infer the spatial and taxonomic distribution of species' vulnerability under each scenario, we calculated the proportion of species in a threat category (Vulnerable, Endangered, Critically Endangered) at a pixel resolution of 30 arc seconds and by family. Our results show a high proportion (58–67%) of threatened species among the four experimental scenarios, with the highest proportion under RCP8.5 and limited dispersal. Threatened species were concentrated in montane areas and avoided lowland areas where conditions are likely to be increasingly inhospitable. Annual precipitation and diurnal temperature range were the main drivers of species' relative vulnerability. Our approach identifies strategic montane areas and taxa of conservation concern that merit urgent inclusion in management plans to improve climatic resilience in the Mesoamerican biodiversity hotspot. Such information is necessary to develop policies that prioritize vulnerable elements and mitigate threats to biodiversity under climate change. [ABSTRACT FROM AUTHOR]

Beef production accounts for the largest share of global livestock greenhouse gas emissions and is an important target for climate mitigation efforts. Most life-cycle assessments comparing the carbon footprint of beef production systems have been limited to production emissions. None also consider potential carbon sequestration due to grazing and alternate uses of land used for production. We assess the carbon footprint of 100 beef production systems in 16 countries, including production emissions, soil carbon sequestration from grazing, and carbon opportunity cost—the potential carbon sequestration that could occur on land if it were not used for production. We conduct a pairwise comparison of pasture-finished operations in which cattle almost exclusively consume grasses and forage, and grain-finished operations in which cattle are first grazed and then fed a grain-based diet. We find that pasture-finished operations have 20% higher production emissions and 42% higher carbon footprint than grain-finished systems. We also find that more land-intensive operations generally have higher carbon footprints. Regression analysis indicates that a 10% increase in land-use intensity is associated with a 4.8% increase in production emissions, but a 9.0% increase in carbon footprint, including production emissions, soil carbon sequestration and carbon opportunity cost. The carbon opportunity cost of operations was, on average, 130% larger than production emissions. These results point to the importance of accounting for carbon opportunity cost in assessing the sustainability of beef production systems and developing climate mitigation strategies. [ABSTRACT FROM AUTHOR]

With the change of social economic system and the rapid growth of agricultural economy in China, the amount of agricultural energy consumption and carbon dioxide emissions has increased dramatically. Based on the estimation of agricultural carbon dioxide emissions from 1991 to 2018 in China, this paper uses EKC model to analyze economic growth and agricultural carbon dioxide emissions. The Kaya method is used to decompose the factors affecting agricultural carbon dioxide emissions. The experimental results show that there is a co-integration relationship between economic growth and the total intensity of agricultural carbon emissions, and between economic growth and the intensity of carbon emissions caused by five types of carbon sources: fertilizer, pesticide, agricultural film, agricultural diesel oil and tillage. Economic growth is the main driving factor of agricultural carbon dioxide emissions. In addition, technological progress has a strong role in promoting carbon emission reduction, but it has a certain randomness. However, the impact of energy consumption structure and population size on carbon emissions is not obvious., Competing Interests: The authors have declared that no competing interests exist.

This paper studies the heterogeneous effects of exchange rate and stock market on carbon emission allowance price in four emissions trading scheme pilots in China. We employ a panel quantile regression model, which can describe both individual and distributional heterogeneity. The empirical results illustrate that the effects of explanatory variables on carbon emission allowance price is heterogeneous along the whole quantiles. Specifically, exchange rate has a negative effect on carbon emission allowance price at lower quantiles, while becomes a positive effect at higher quantiles. In addition, a negative effect exists between domestic stock market and carbon emission allowance price, and the intensity decreasing along with the increase of quantile. By contrast, an increasing positive effect is discovered between European stock market and domestic carbon emission allowance prices. Finally, heterogeneous effects on carbon emission allowance price can also be proved in European Union Emission Trading Scheme (EU-ETS). [ABSTRACT FROM AUTHOR]

It is estimated that about 1/4th of all greenhouse gas (GHG) emissions may be caused by the global food system. Reducing the GHG emissions from food production is a major challenge in the context of the projected growth of the world's population, which is increasing demand for food. In this context, the goal should be to achieve the lowest possible emission intensity of the food production system, understood as the amount of GHG emissions per unit of output. The study aimed to calculate the emission intensity of food production systems and to specify its determinants based on a panel regression model for 14 countries, which accounted for more than 65% of food production in the world between 2000 and 2014. In this article, emission intensity is defined as the amount of GHG emissions per value of global output. Research on the determinants of GHG emissions related to food production is well documented in the literature; however, there is a lack of research on the determinants of the emission intensity ratio for food production. Hence, the original contribution of this paper is the analysis of the determinants of GHG emissions intensity of food production systems. The study found the decreased of emission intensity from an average of more than 0.68 kg of CO2 equivalent per USD 1 worth of food production global output in 2000 to less than 0.46 in 2014. The determinants of emission intensity decrease included the yield of cereals, the use of nitrogen fertilizers, the agriculture material intensity, the Human Development Index, and the share of fossil fuel energy consumption in total energy use. The determinants of growth of emission intensity of food production systems included GDP per capita, population density, nitrogen fertilizer production, utilized agriculture area, share of animal production, and energy use per capita., Competing Interests: The authors have declared that no competing interests exist.

A critical examination of the US Environmental Protection Agency's (US EPA's) Greenhouse Gas Reporting Program (GHGRP) database provided an opportunity for the largest evaluation to date of landfilled waste decomposition kinetics with respect to different US climate regimes. In this paper, 5-8 years of annual methane collection data from 114 closed landfills located in 29 states were used to estimate site-specific waste decay rates (k) and methane collection potentials (Lc). These sites account for approximately 9% of all landfills required to report GHG emissions to the US EPA annually. The mean methane collection potential (Lc) for the sites located in regions with less than 635 mm (25 in) annual rainfall was significantly (p<0.002) lower than the mean methane collection potential of the sites located in regions with more than 635 mm (25 in) annual precipitation (49 and 73 m3 methane Mg-1 waste, respectively). This finding suggests that a fraction of the in-place biodegradable waste may not be decomposing, potentially due to a lack of adequate moisture content of landfills located in arid regions. The results of this evaluation offer insight that challenges assumptions of the traditional landfill methane estimation approach, especially in arid climates, that all methane corresponding to the total methane generation potential of the buried solid waste will be produced. Decay rates showed a significant correlation with annual precipitation, with an average k of 0.043 year-1 for arid regions (< 508 mm (20 in) year-1), 0.074 year-1 for regions with 508-1,016 mm (20-40 in) annual precipitation, and 0.09 year-1 in wet regions (> 1,016 mm (40 in) year-1). The data suggest that waste is decaying faster than the model default values, which in turn suggests that a larger fraction of methane is produced during a landfill's operating life (relative to post-closure)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The employment of Pradeep Jain and James Wally with Innovative Waste Consulting Services LLC does not alter our adherence to PLOS ONE policies on sharing data and materials.

Economic, environmental, and social effects are the most dominating issues in cold chain logistics. The goal of this paper is to propose a cost-saving, energy-saving, and emission-reducing bi-objective model for the cold chain-based low-carbon location-routing problem. In the proposed model, the first objective (economic and environmental effects) is to minimize the total logistics costs consisting of costs of depots to open, renting vehicles, fuel consumption, and carbon emission, and the second one (social effect) is to reduce the damage of cargos, which could improve the client satisfaction. In the proposed model, a strategy is developed to meet the requirements of clients as to the demands on the types of cargos, that is, general cargos, refrigerated cargos, and frozen cargos. Since the proposed problem is NP-hard, we proposed a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms (MOEAs). Furthermore, in the experiments, we first examined the effectiveness of the proposed framework by assessing the performance of seven MOEAs, and also verified the efficiency of the proposed model. Extensive experiments were carried out to investigate the effects of the proposed strategy and variants on depot capacity, hard time windows, and fleet composition on the performance indicators of Pareto fronts and cold chain logistics networks, such as fuel consumption, carbon emission, travel distance, travel time, and the total waiting time of vehicles., Competing Interests: The authors have declared that no competing interests exist.

The selection of seismic attributes is a key process in reservoir prediction because the prediction accuracy relies on the reliability and credibility of the seismic attributes. However, effective selection method for useful seismic attributes is still a challenge. This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). The proposed method has a two-hierarchical structure. In the first hierarchy, the primary selection of seismic attributes is achieved by calculating the GRD between seismic attributes and reservoir parameters, and the GRD between the seismic attributes. The principle of the primary selection is that these seismic attributes with higher GRD to the reservoir parameters will have smaller GRD between themselves as compared to those with lower GRD to the reservoir parameters. Then the SVM is employed in the second hierarchy to perform an interactive error verification using training samples for the purpose of determining the final seismic attributes. A real-world case study was conducted to evaluate the proposed GRD-SVM method. Reliable seismic attributes were selected to predict the coalbed methane (CBM) content in southern Qinshui basin, China. In the analysis, the instantaneous amplitude, instantaneous bandwidth, instantaneous frequency, and minimum negative curvature were selected, and the predicted CBM content was fundamentally consistent with the measured CBM content. This real-world case study demonstrates that the proposed method is able to effectively select seismic attributes, and improve the prediction accuracy. Thus, the proposed GRD-SVM method can be used for the selection of seismic attributes in practice. [ABSTRACT FROM AUTHOR]

China's transport sector is facing enormous challenges from soaring energy consumption and greenhouse gas (GHG) emissions. Transport electrification has been viewed as a major solution to transportation decarbonization, and electric vehicles (EVs) have attracted considerable attention from policymakers. This paper analyzes the effects of the introduction of EVs in China. A system dynamics model is developed and applied to assess the energy-saving and emission-reducing impacts of the projected penetration of EVs until the year 2030. Five types of scenarios of various EV penetration rates, electricity generation mixes, and the speed of technological improvement are discussed. Results confirm that reductions in transport GHG emissions and gasoline and diesel consumption by 3.0%-16.2%, 4.4%-16.1%, and 15.8%-34.3%, respectively, will be achieved by 2030 under China's projected EV penetration scenarios. Results also confirm that if EV penetration is accompanied by decarbonized electricity generation, that is, the use of 55% coal by 2030, then total transport GHG emissions will be further reduced by 0.8%-4.4%. Moreover, further reductions of GHG emissions of up to 5.6% could be achieved through technological improvement. The promotion of EVs could substantially affect the reduction of transport GHG emissions in China, despite the uncertainty of the influence intensity, which is dependent on the penetration rate of EVs, the decarbonization of the power sector, and the technological improvement efficiency of EVs and internal combustion engine vehicles., Competing Interests: The authors have declared that no competing interests exist.

Maintaining crop outputs to feed its large population with limited resources while simultaneously mitigating carbon emissions are great challenges for China. Improving the efficiency of resource use in crop production is important in reducing carbon emissions. This paper constructs a methodological framework combining emergy-based indicator accounting and a nonseparable undesirable output slack-based measurement (SBM) data envelopment analysis (DEA) model. This framework is used to explore the efficiency of inputs and outputs and the greenhouse gas (GHG) emissions reduction potential for crop production systems, using Zhejiang province, China, as a case study. It is found that an emergy synthesis and a nonseparable undesirable output SBM-DEA framework is compatible with the case study. Crop production in Zhejiang province has relied heavily on an increase in agrochemical inputs to maintain agricultural output. Energy and chemical fertilizer use are determined as the province's major carbon emissions sources. Although carbon emissions per unit of monetary output has decreased sharply, the carbon emissions per unit emergy output has increased, demonstrating a high carbon intensity reality. The DEA highlighted the differences in crop production efficiency, resource factor redundancy and carbon mitigation potential in the different prefectures of the province. To conclude this research, policies to support low carbon agriculture development, including subsidizing low carbon agriculture technology development and expansion and the cancellation of subsidies to high carbon production factors, such as chemical fertilizer production and sales, are discussed to conclude the research., Competing Interests: The authors have declared that no competing interests exist.

Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or "circularity", means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure. [ABSTRACT FROM AUTHOR]

Compared with the idealized isotropic coal seam, the effectively cracking distances of deep-hole pre-splitting blasting in different directions have significant difference in the structural anisotropy of coal seam. In this paper, based on the test results of coal mechanical parameters in different coring directions, the blasting crack distances in these coring directions of coal seam are theoretically calculated and analyzed, and then a new method of blasthole layout in coal seam is designed and the blasting crack effect is investigated according to the test data of gas drainage. The research results show that both compressive strength and tensile strength of the direction of perpendicular to bedding are larger than that in other directions, especially parallel to bedding. The blasting crack distances of calculation results are in great agreement with that of underground in-situ measurement, so the blasting crack zone caused by the differences of mechanical properties in different directions can be approximately treated as an ellipse. Comparing the conventional method of blasthole layout, the blasting crack effect with new method has greatly enhanced the methane concentration and gas drainage quantity. Pure methane quantities with the new method and conventional method are about 2.8 times and 1.67 times as large as that before blasting, and pure methane quantity with the new method is about 1.46 times that with the conventional method. Therefore, considering the anisotropic characteristics of coal seams, the reasonable blasthole layout plays an important role for enhancing gas drainage in low permeability and outburst working face. [ABSTRACT FROM AUTHOR]

Understanding the causality between energy consumption, carbon emissions and economic growth is helpful for policymakers to formulate energy, environmental and economic policies. For the first time, based on nonlinear dynamics, this paper employs multispatial convergent cross mapping (CCM) to revisit the energy-carbon-economy causation for China, India and the G7 countries using both aggregate data and per capita data. The findings indicate that there are significant differences between developing countries and developed countries. A bidirectional nexus between energy consumption, carbon emissions and economic growth is found in China and India, but various causal relationships are identified in the G7 countries, including bidirectional, unidirectional and neutral nexus. The results confirm that the decoupling phenomenon is common in most G7 countries. By leveraging a variety of samples and a new approach, this study provides new evidence for policy authorities to formulate country-specific policies to obtain better environmental quality while achieving sustainable economic growth. [ABSTRACT FROM AUTHOR]

This paper aims to develop a novel computational technique for the detection of the transit through the anaerobic threshold. This technique uses only cardiac intervals derived from the electrocardiogram and is based on algebraic relationships between RR and QRS intervals. Electrocardiograms are measured during the load and the recovery processes. Algebraic relationships between cardiac intervals are used not only to identify the anaerobic threshold but also to characterise individual features of the person during the transit through the threshold. The ratio between carbon dioxide and oxygen in the exhaled air is used to validate the results. The algebraic relationship between cardiac intervals serves as a stand-alone indicator for both the determination of the anaerobic threshold and the characterization of the performance of the person during the load and the recovery processes. [ABSTRACT FROM AUTHOR]

The production of concrete for construction purposes is a major source of anthropogenic CO2 emissions. One promising avenue towards a more sustainable construction industry is to make use of naturally occurring mineral-microbe interactions, such as microbial-induced carbonate precipitation (MICP), to produce solid materials. In this paper, we present a new process where calcium carbonate in the form of powdered limestone is transformed to a binder material (termed BioZEment) through microbial dissolution and recrystallization. For the dissolution step, a suitable bacterial strain, closely related to Bacillus pumilus, was isolated from soil near a limestone quarry. We show that this strain produces organic acids from glucose, inducing the dissolution of calcium carbonate in an aqueous slurry of powdered limestone. In the second step, the dissolved limestone solution is used as the calcium source for MICP in sand packed syringe moulds. The amounts of acid produced and calcium carbonate dissolved are shown to depend on the amount of available oxygen as well as the degree of mixing. Precipitation is induced through the pH increase caused by the hydrolysis of urea, mediated by the enzyme urease, which is produced in situ by the bacterium Sporosarcina pasteurii DSM33. The degree of successful consolidation of sand by BioZEment was found to depend on both the amount of urea and the amount of glucose available in the dissolution reaction. [ABSTRACT FROM AUTHOR]

The data collected by mobile methane (CH4) sensors can be used to find natural gas (NG) leaks in urban distribution systems. Extracting actionable insights from the large volumes of data collected by these sensors requires several data processing steps. While these survey platforms are commercially available, the associated data processing software largely constitute a black box due to their proprietary nature. In this paper we describe a step-by-step algorithm for developing leak indications using data from mobile CH4 surveys, providing an under-the-hood look at the choices and challenges associated with data analysis. We also describe how our algorithm has evolved over time, and the data-driven insights that have prompted these changes. Applying our algorithm to data collected in 15 cities produced more than 6100 leak indications and estimates of the leaks’ size. We use these results to characterize the distribution of leak sizes in local NG distribution systems. Mobile surveys are already an effective and necessary tool for managing NG distribution systems, but improvements in the technology and software will continue to increase its value. [ABSTRACT FROM AUTHOR]

The energy consumption-growth nexus has been widely studied in the empirical literature, though results have been inconclusive regarding the direction, or even the existence, of causality. These inconsistent results can be explained by two important limitations of the literature. First, the use of bivariate models, which fail to detect more complex causal relations, or the ad hoc approach to selecting variables in a multivariate framework; and, second, the use of linear causal models, which are unable to capture more complex nonlinear causal relationships. In this paper, we aim to overcome both limitations by analysing the energy consumption-growth nexus using a Flexible Fourier form due to Enders and Jones (2016). The analysis focuses on the US over the period 1949 to 2014. From our results we can conclude that, where the linear methodology supports the neutrality hypothesis (no causality between energy consumption and growth), the Flexible Fourier form points to the existence of causality from energy consumption to growth. This is contrary to the linear analysis, suggesting that lowering energy consumption would adversely affect US economic growth. Thus, by employing the Flexible Fourier form we find the conclusions can be quite different. [ABSTRACT FROM AUTHOR]

This paper demonstrates the impact of an endemic fossorial animal, plateau pika (Ochotona curzoniae), on soil properties and N2O flux at the Zoige Wetland. Pika burrow and control sites without disturbance by pika were selected to measure the soil water content, bulk density, soil organic matter (SOM), NH4-N content and NO3-N content in August 2012. N2O fluxes were measured with static opaque chambers at these sites in June and August 2012. Pika burrowing altered soil aeration by transferring deeper soil to the surface and by constructing underground burrows, which significantly increased bulk density, and reduced soil water content, SOM and NH4-N content at 0–10 cm and 10–20 cm soil depth. N2O flux had a significant correlation with bulk density, SOM and NH4-N content. Pika burrowing significantly influenced N2O flux by increasing N2O flux at the control site from near zero to 0.063±0.011 mg m-2 h-1. Our findings described how pika burrowing influences the soil traits and significantly increases the principal greenhouse gas N2O emission. As plateau pika was commonly considered as a pest, our findings give a novel clue to effectively manage populations of plateau pika on the Qinghai-Tibet Plateau from the perspective of greenhouse gas emission. [ABSTRACT FROM AUTHOR]

This paper presents the results of research into pathways leading to the production of methane (CH4) and carbon dioxide (CO2) in sediments of two eutrophic reservoirs (Maziarnia and Nielisz), located in south-eastern Poland. In seeking to identify the pathways in question, use was made of analysis of stable carbon isotopes in CH4 and CO2 dissolved in pore water. This determined that CH4 is mainly produced through acetate fermentation, though the hydrogenotrophic methanogenic process may also be of importance, especially in deeper layers of sediments. Both the presence of autochthonous organic matter and increased pH values are shown to favour acetate fermentation. In turn, methanogenesis in sediments is assessed as capable of accounting for the generation of a considerable amount of CO2. Indeed, the role of methanogenesis in CO2 production is increasingly important further down in the layers of sediment, where allochthonous organic matter is predominant. [ABSTRACT FROM AUTHOR]

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. [ABSTRACT FROM AUTHOR]

Super-critical carbon dioxide energy-storage (SC-CCES) technology is a new type of gas energy-storage technology. This paper used orthogonal method and variance analysis to make significant analysis on the factors which would affect the thermodynamics characteristics of the SC-CCES system and obtained the significant factors and interactions in the energy-storage process, the energy-release process and the whole energy-storage system. Results have shown that the interactions in the components have little influence on the energy-storage process, the energy-release process and the whole energy-storage process of the SC-CCES system, the significant factors are mainly on the characteristics of the system component itself, which will provide reference for the optimization of the thermal properties of the energy-storage system. [ABSTRACT FROM AUTHOR]

The paper presents the results of an experiment on the effect of pre-sowing stimulation of seeds with atmospheric pressure plasma jet operating with dielectric barrier discharge (DBD plasma jet) on the process of germination of Thuringian Mallow (Lavatera thuringiaca L.). Five groups of seeds characterized by a different exposure times (1, 2, 5, 10 and 15 minutes) as well as untreated seeds—control were used. Pre-sowing plasma stimulation of seeds improved germination parameters such as: germination capacity and germination energy for all tested groups relative to control. The highest germination parameters were obtained for seeds stimulated with plasma for the exposure times of 2 and 5 min. The analysis of the contact surface angle indicated the decrease of its’ mean values upon seed stimulation while no statistical effects were observed. Analysis of the SEM scans revealed the increase in seed pattern intensity which could be attributed to removing of the surface parts of cuticle possibly covered with wax upon short time—2 and 5 min plasma treatment. Such a phenomenon can act similarly to mechanical scarification of seeds. Longer exposure of seeds to plasma resulted in affecting the deeper zone of cuticle and damage or fracture of some parts of the cuticle. Lower germination parameters of seeds upon longer exposure times to plasma may indicate mechanical damage of the seeds. [ABSTRACT FROM AUTHOR]

Fertilizer application is the basis for ensuring high yield, high quality and high efficiency of farmland. In order to meet the demand for food with the increasing of population, the application of nitrogen fertilizer will be further increased, which will lead to problems such as N2O emission and nitrogen loss from farmland, it will easily deteriorate the soil and water environment of farmland, and will not conducive to the sustainable development of modern agriculture. However, optimizing fertilizer management is an important way to solve this problem. While, due to the differences in the study conditions (geographical location, environmental conditions, experimental design, etc.), leading to the results obtained in the literatures about the N2O emission with different nitrogen fertilizer application strategies have significant differences, which requiring further comprehensive quantitative analysis. Therefore, we analyzed the effects of nitrogen fertilizer application strategies (different fertilizer types and fertilizer application rates) on N2O emissions from the fields (rice, wheat and maize) based on the Meta-analysis using 67 published studies (including 1289 comparisons). For the three crops, inorganic fertilizer application significantly increased on-farm N2O emissions by 19.7–101.05% for all three; and organic fertilizer increased N2O emissions by 28.16% and 69.44% in wheat and maize fields, respectively, but the application of organic fertilizer in rice field significantly reduced N2O emissions by 58.1%. The results showed that overall, the application of inorganic fertilizers resulted in higher N2O emissions from farmland compared to the application of organic fertilizers. In addition, in this study, the average annual temperature, annual precipitation, soil type, pH, soil total nitrogen content, soil organic carbon content, and soil bulk weight were used as the main influencing factors of N2O emission under nitrogen fertilizer strategies, and the results of the study can provide a reference for the development of integrated management measures to control greenhouse gas emissions from agricultural soils. [ABSTRACT FROM AUTHOR]

Lake ice is an important socio-economic resource that is threatened by climate change. The cover and duration of lake ice are expected to decline as air temperatures warm in the coming decades, disrupting a previously reliable source of income for many activities dependent on lake ice. The economic consequences of climate-induced lake ice loss remain unexplored, creating a significant research gap. The purpose of this study was to quantify the monetary spending associated with lake ice and how climate change may impact that value. Using a series of General Circulation Models (GCMs), greenhouse gas emissions scenarios, and models for lake ice cover, we predicted changes in lake ice by the end of the 21st century for the Northern Hemisphere. We also synthesized examples of spending associated with lake ice activities and discussed the potential implications expected with declining ice cover. We found that lake ice will decrease in area by 44,000–177,000 km2 and shorten in duration by 13–43 days by 2100. Using 31 examples of revenue from lake ice, we found that lake ice generates spending of over USD 2.04 billion to local communities and economies. We also found that countries predicted to experience the greatest ice loss by the end of the century are those that currently have the largest GDP, highest greenhouse gas emissions, and are most dependent on freshwater withdrawal. Our findings confirm predicted losses in lake ice that are expected because of climate change and quantify some of the potential consequences for local communities. Here we highlight lake ice as another casualty of human-caused climate change that will have profound socio-economic implications. [ABSTRACT FROM AUTHOR]

Forest ecosystems store large amounts of carbon and can be important sources, or sinks, of the atmospheric carbon dioxide that is contributing to global warming. Understanding the carbon storage potential of different forests and their response to management and disturbance events are fundamental to developing policies and scenarios to partially offset greenhouse gas emissions. Projections of live tree carbon accumulation are handled differently in different models, with inconsistent results. We developed growth-and-yield style models to predict stand-level live tree carbon density as a function of stand age in all vegetation types of the coastal Pacific region, US (California, Oregon, and Washington), from 7,523 national forest inventory plots. We incorporated site productivity and stockability within the Chapman-Richards equation and tested whether intensively managed private forests behaved differently from less managed public forests. We found that the best models incorporated stockability in the equation term controlling stand carrying capacity, and site productivity in the equation terms controlling the growth rate and shape of the curve. RMSEs ranged from 10 to 137 Mg C/ha for different vegetation types. There was not a significant effect of ownership over the standard industrial rotation length (~50 yrs) for the productive Douglas-fir/western hemlock zone, indicating that differences in stockability and productivity captured much of the variation attributed to management intensity. Our models suggest that doubling the rotation length on these intensively managed lands from 35 to 70 years would result in 2.35 times more live tree carbon stored on the landscape. These findings are at odds with some studies that have projected higher carbon densities with stand age for the same vegetation types, and have not found an increase in yields (on an annual basis) with longer rotations. We suspect that differences are primarily due to the application of yield curves developed from fully-stocked, undisturbed, single-species, "normal" stands without accounting for the substantial proportion of forests that don't meet those assumptions. The carbon accumulation curves developed here can be applied directly in growth-and-yield style projection models, and used to validate the predictions of ecophysiological, cohort, or single-tree style models being used to project carbon futures for forests in the region. Our approach may prove useful for developing robust models in other forest types. [ABSTRACT FROM AUTHOR]

An understanding of species-environmental relationships is invaluable for effective conservation and management under anthropogenic climate change, especially for biodiversity hotspots such as riparian habitats. Species distribution models (SDMs) assess present species-environmental relationships which can project potential suitable environments through space and time. An understanding of environmental factors associated with distributions can guide conservation management strategies under a changing climate. We generated 260 ensemble SDMs for five species of Thamnophis gartersnakes (n = 347)—an important riparian predator guild—in a semiarid and biogeographically diverse region under impact from climate change (Arizona, United States). We modeled present species-environmental relationships and projected changes to suitable environment under 12 future climate scenarios per species, including the most and least optimistic greenhouse gas emission pathways, through 2100. We found that Thamnophis likely advanced northward since the turn of the 20th century and overwinter temperature and seasonal precipitation best explained present distributions. Future ranges of suitable environment for Thamnophis are projected to decrease by ca. -37.1% on average. We found that species already threatened with extinction or those with warm trailing-edge populations likely face the greatest loss of suitable environment, including near or complete loss of suitable environment. Future climate scenarios suggest an upward advance of suitable environment around montane areas for some low to mid-elevation species, which may create pressures to ascend. The most suitable environmental areas projected here can be used to identify potential safe zones to prioritize conservation refuges, including applicable critical habitat designations. By bounding the climate pathway extremes to, we reduce SDM uncertainties and provide valuable information to help conservation practitioners mitigate climate-induced threats to species. Implementing informed conservation actions is paramount for sustaining biodiversity in important aridland riparian systems as the climate warms and dries. [ABSTRACT FROM AUTHOR]

This paper presents a forecast and analysis of population, economic development, energy consumption and CO2 emissions variation in China in the short- and long-term steps before 2020 with 2007 as the base year. The widely applied IPAT model, which is the basis for calculations, projections, and scenarios of greenhouse gases (GHGs) reformulated as the Kaya equation, is extended to analyze and predict the relations between human activities and the environment. Four scenarios of CO2 emissions are used including business as usual (BAU), energy efficiency improvement scenario (EEI), low carbon scenario (LC) and enhanced low carbon scenario (ELC). The results show that carbon intensity will be reduced by 40–45% as scheduled and economic growth rate will be 6% in China under LC scenario by 2020. The LC scenario, as the most appropriate and the most feasible scheme for China’s low-carbon development in the future, can maximize the harmonious development of economy, society, energy and environmental systems. Assuming China's development follows the LC scenario, the paper further gives four paths of low-carbon transformation in China: technological innovation, industrial structure optimization, energy structure optimization and policy guidance. [ABSTRACT FROM AUTHOR]

Background: Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods: We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results: The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions: From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. [ABSTRACT FROM AUTHOR]

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA. [ABSTRACT FROM AUTHOR]

Background: Activities embedded in academic culture (international conferences, field missions) are an important source of greenhouse gas emissions. For this reason, collective efforts are still needed to lower the carbon footprint of Academia. Serious games are often used to promote ecological transition. Nevertheless, most evaluations of their effects focus on changes in knowledge and not on behaviour. The main objectives of this study are to 1) Evaluate the feasibility of a control and an experimental behaviour change intervention and, 2) Evaluate the fidelity (the extent to which the implementation of the study corresponds to the original design) of both interventions. Methods: People employed by a French research organisation (N = 30) will be randomised to one of the two arms. The experimental arm consists in a 1-hour group discussion for raising awareness about climate change, carrying out a carbon footprint assessment and participating to a serious game called "Ma terre en 180 minutes." The control arm consists of the same intervention (1h discussion + carbon footprint assessment) but without participating to the serious game. On two occasions over one month, participants will be asked to fill in online surveys about their behaviours, psychological constructs related to behaviour change, sociodemographic and institutional information. For every session of intervention, the facilitators will assess task completion, perceived complexity of the tasks and the perceived responsiveness of participants. Descriptive statistics will be done to analyse percentages and averages of the different outcomes. Discussion: Ma-terre EVAL pilot study is a 1-month and a half pilot randomised controlled trial aiming to evaluate the feasibility and the fidelity of a 24-month randomised controlled trial. This study will provide more information on the levers and obstacles to reducing the carbon footprint among Academia members, so that they can be targeted through behaviour change interventions or institutional policies. [ABSTRACT FROM AUTHOR]

Earthworms can stimulate microbial activity and hence greenhouse gas (GHG) emissions from soils. However, the extent of this effect in the presence of plants and soil moisture fluctuations, which are influenced by earthworm burrowing activity, remains uncertain. Here, we report the effects of earthworms (without, anecic, endogeic, both) and plants (with, without) on GHG (CO2, N2O) emissions in a 3-month greenhouse mesocosm experiment simulating a simplified agricultural context. The mesocosms allowed for water drainage at the bottom to account for the earthworm engineering effect on water flow during two drying-wetting cycles. N2O cumulative emissions were 34.6% and 44.8% lower when both earthworm species and only endogeic species were present, respectively, and 19.8% lower in the presence of plants. The presence of the endogeic species alone or in combination with the anecic species slightly reduced CO2 emissions by 5.9% and 11.4%, respectively, and the presence of plants increased emissions by 6%. Earthworms, plants and soil water content interactively affected weekly N2O emissions, an effect controlled by increased soil dryness due to drainage via earthworm burrows and mesocosm evapotranspiration. Soil macroporosity (measured by X-ray tomography) was affected by earthworm species-specific burrowing activity. Both GHG emissions decreased with topsoil macropore volume, presumably due to reduced moisture and microbial activity. N2O emissions decreased with macropore volume in the deepest layer, likely due to the presence of fewer anaerobic microsites. Our results indicate that, under experimental conditions allowing for plant and earthworm engineering effects on soil moisture, earthworms do not increase GHG emissions, and endogeic earthworms may even reduce N2O emissions. [ABSTRACT FROM AUTHOR]