Global and regional drivers of land-use emissions in 1961-2017

Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions

Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.

Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O

An in-depth analysis of the role of greenhouse gases (GHGs) in climate change is examined here along with their diverse sources, including the combustion of fossil fuels, agriculture, and industrial processes. Key GHG components such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are considered, along with data on emissions across various economic sectors. The consequences of climate change are also highlighted, ranging from more frequent and intense extreme weather events to rising sea levels and impacts on ecosystems and human health. The industrial revolution and unrestricted use of fossil fuels are key factors leading to an increase in GHG concentrations in the atmosphere. Global efforts to reduce emissions are considered, starting with the 1997 Kyoto Protocol and culminating in the 2015 Paris Agreement. The limited effectiveness of early initiatives is underscored, emphasizing the significant importance of the Paris Agreement that provides a global framework for establishing goals to reduce GHG emissions by country. The Green Climate Fund and other international financial mechanisms are also considered as essential tools for financing sustainable projects in developing countries. The global community faces the challenge and necessity for more ambitious efforts to achieve the set goals for reducing GHG emissions. Successful strategies are examined by Sweden, Costa Rica, and Denmark to achieve zero GHG emissions that integrate renewable energy sources and technologies. The importance of global cooperation for creating a sustainable future is also emphasized.

Potential benefits of cropping pattern change in the climate-sensitive regions of rice production in China

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region.

Chinese cities show different trend toward carbon peak

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

Farmer's climate smart livestock production adoption and determinant factors in Hidebu Abote District, Central Ethiopia

This study aimed to identify the status, determining factors, and challenges in adopting climate smart livestock production practices by farmers. Three-staged sampling techniques were used to select the research sites and 233 sample farmer household respondents. Data were collected mainly using a pre-tested structured questionnaire. Key informant interviews and focus group discussions were also conducted to complement the household survey data. Descriptive statistics and an ordered logistic regression model were applied to analyze the quantitative data. The result revealed that the most adopted practices were composting (85.41%) and manure management (70.39%) while the least adopted technologies were biogas generation (3.86%) and rotation grazing (22.32%). The adoption status of the sampled farmers was also categorized into low (19.74%), medium (67.81%), and high adopter (12.45%). The high cost of improved breed, use of manure for fuel, free grazing, lack of information and awareness were the major constraints to adopting the climate smart livestock production technologies. The result also revealed that education, grazing land, total livestock holding, and extension agent contact contributed significantly and positively to the adoption of smart livestock production technology, while the distance from the water source had an insignificant and negative effect on the adoption status of climate smart livestock production practices. The study suggests the relevance of the cooperation of stakeholders and strengthening extension services for the maximum benefits of climate smart livestock production.

Dynamics of carbon storage driven by land use/land cover transformation in coal mining areas with a high groundwater table: A case study of Yanzhou Coal Mine, China

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985-1995, 1995-2005, 2005-2015, and 2015-2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management.

Global energy use and carbon emissions from irrigated agriculture

Irrigation is a land management practice with major environmental impacts. However, global energy consumption and carbon emissions resulting from irrigation remain unknown. We assess the worldwide energy consumption and carbon emissions associated with irrigation, while also measuring the potential energy and carbon reductions achievable through the adoption of efficient and low-carbon irrigation practices. Currently, irrigation contributes 216 million metric tons of CO2 emissions and consumes 1896 petajoules of energy annually, representing 15% of greenhouse gas emissions and energy utilized in agricultural operations. Despite only 40% of irrigated agriculture relies on groundwater sources, groundwater pumping accounts for 89% of the total energy consumption in irrigation. Projections indicate that future expansion of irrigation could lead to a 28% increase in energy usage. Embracing highly efficient, low-carbon irrigation methods has the potential to cut energy consumption in half and reduce CO2 emissions by 90%. However, considering country-specific feasibility of mitigation options, global CO2 emissions may only see a 55% reduction. Our research offers comprehensive insights into the energy consumption and carbon emissions associated with irrigation, contributing valuable information that can guide assessments of the viability of irrigation in enhancing adaptive capacity within the agricultural sector.

Sustainable Blue Foods from Rice-Animal Coculture Systems

Global interest grows in blue foods as part of sustainable diets, but little is known about the potential and environmental performance of blue foods from rice-animal coculture systems. Here, we compiled a large experimental database and conducted a comprehensive life cycle assessment to estimate the impacts of scaling up rice-fish and rice-crayfish systems in China. We find that a large amount of protein can be produced from the coculture systems, equivalent to ∼20% of freshwater aquaculture and ∼70% of marine wild capture projected in 2030. Because of the ecological benefits created by the symbiotic relationships, cocultured fish and crayfish are estimated to be carbon-negative (-9.8 and -4.7 kg of CO2e per 100 g of protein, respectively). When promoted at scale to displace red meat, they can save up to ∼98 million tons of greenhouse gases and up to ∼13 million hectares of farmland, equivalent to ∼44% of China's total rice acreage. These results suggest that rice-animal coculture systems can be an important source of blue foods and contribute to a sustainable dietary shift, while reducing the environmental footprints of rice production. To harvest these benefits, robust policy supports are required to guide the sustainable development of coculture systems and promote healthy and sustainable dietary change.

Upgrading Passenger Vehicle Emission Standard Helps to Reduce China's Air Pollution Risk from Uncertainty in Electrification

Upgrading to the CHINA 7 standard is crucial for managing air pollution from passenger vehicles in China. Meanwhile, China aims to achieve carbon neutrality by 2060, which necessitates large-scale replacement of gasoline vehicles with electric vehicles in the future. Consequently, the public might view upgrading gasoline vehicles to the CHINA 7 standard as redundant. However, the emission reduction benefits of upgrading standards in the context of uncertain electrification ambitions have not received adequate attention. Here, we show that upgrading standards will compensate for the absence of emissions reductions due to hindered electrification efforts. In the best scenario, China's CO2 emissions can be reduced to 0.047 Gt and NOx to 8.2 × 103 t in 2050. In nonextreme electrification scenarios with CHINA 7 standard, the emission intensity reduction will remain the main driver for emission reductions, outweighing the electrification contribution. In extreme electrification scenarios, upgrading standards will tackle the increased emissions from plug-in hybrid electric vehicles. Our fleet-level results advocate for early standards upgrades to enhance resilience against air pollution risks arising from uncertainties in electrification. Our evidence from China, with one of the most stringent emission standards, can provide a reference point for the world on the upgrading passenger vehicle emission standard issue.

Boosting CO2 chemical fixation over MOF-808 by the introduction of functional groups and defective Zr sites

CO2 cycloaddition has emerged as a promising approach for producing value-added cyclocarbonates and mitigating greenhouse gas emissions. Although MOF-808 serves as a stable catalyst for cycloaddition, its limited activity constrains broader applications. Through the modification with a fluoride group via a ligand exchange method, F-MOF-808-1.5 exhibits exceptional performance, achieving a 98.8% conversion with 97.8% selectivity to epichlorohydrin carbonate-marking a substantial 100% improvement compared to pristine MOF-808. The defective Zr sites and the electron-withdrawing groups synergistically promote the ring opening of epoxides. Furthermore, the catalyst demonstrates high stability over multiple reaction cycles. Notably, without adding solvents and co-catalysts, F-MOF-808-1.5 outperforms most reported MOF-based catalysts.

Twelve-year conversion of rice paddy to wetland does not alter SOC content but decreases C decomposition and N mineralization in Japan

Land-use change worldwide has been driven by anthropogenic activities, which profoundly regulates terrestrial C and N cycles. However, it remains unclear how the dynamics and decomposition of soil organic C (SOC) and N respond to long-term conversion of rice paddy to wetland. Here, soil samples from five soil depths (0-25 cm, 5 cm/depth) were collected from a continuous rice paddy and an adjacent wetland (a rice paddy abandoned for 12 years) on Shonai Plain in northeastern Japan. A four-week anaerobic incubation experiment was conducted to investigate soil C decomposition and N mineralization. Our results showed that SOC in the wetland and rice paddy decreased with soil depth, from 31.02 to 19.66 g kg-1 and from 30.26 to 18.86 g kg-1, respectively. There was no significant difference in SOC content between wetland and rice paddy at any depth. Soil total nitrogen (TN) content in the wetland (2.61-1.49 g kg-1) and rice paddy (2.91-1.78 g kg-1) showed decreasing trend with depth; TN was significantly greater in the rice paddy than in the wetland at all depths except 20-25 cm. Paddy soil had significantly lower C/N ratios but significantly larger decomposed C (Dec-C, CO2 and CH4 production) and mineralized N (Min-N, net NH4+-N production) than wetland soil across all depths. Moreover, the Dec-C/Min-N ratio was significantly larger in wetland than in rice paddy across all depths. Rice paddy had higher exponential correlation between Dec-C and SOC, Min-N and TN than wetland. Although SOC did not change, TN decreased by 14.1% after the land-use conversion. The Dec-C and Min-N were decreased by 32.7% and 42.2%, respectively, after the12-year abandonment of rice paddy. Conclusively, long-term conversion of rice paddy to wetland did not distinctly alter SOC content but increased C/N ratio, and decreased C decomposition and N mineralization in 0-25 cm soil depth.

The spatial and source heterogeneity of agricultural emissions highlight necessity of tailored regional mitigation strategies

Agriculture contributes considerable greenhouse gas emissions while feed the constantly expanding world population. The challenge of balancing food security with emissions reduction to create a mutually beneficial situation is paramount. However, assessing targeted mitigation potential for agricultural emissions remains challenging, lacking comprehensive sub-national evaluations. Here, we have meticulously compiled the agricultural greenhouse gas emission inventories of China spanning the years 2000 to 2019, employing spatial analysis techniques to identify regional characteristics. We find that the peak of China's agricultural production emissions occurred in 2015 (1.03 × 109 tCO2 equivalent), followed by a valley in 2019 (0.94 tCO2 equivalent), largely attributed to shifts in livestock-related activities. Notably, methane emissions were the most dominant greenhouse gas, the Hunan province emerged as a prominent contributor, livestock raising stood out as a major activity, and enteric fermentation ranked as the primary emission source. There were substantial differences in the emission structure and sources among the provinces. Further spatial analysis showed geographical disparities in both total emissions and per capita emissions. The west-east blocked spatial characteristics of per capita emissions at the Hu Line sides emerged. We advocate that tailored mitigation strategy focusing on specific emission sources and regions can achieve substantial progress with minimal effort.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Health burden from food systems is highly unequal across income groups

Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Construction of a methodology framework to characterize dynamic full-sector land-use carbon emissions embodied in trade

The globally massive land-use changes associated with unprecedented urbanization rate are leading to prodigious quantities of carbon emissions. Nonetheless, the dynamics of land-use carbon emissions, particularly driven by supply-chain activities across all relevant industrial sectors, remain largely unexplored, especially in non-agricultural sectors. Here, we constructed a novel methodological framework to quantify full-sector land-use carbon emissions in Shenzhen, China, an international megacity grappling with acute land resource scarcity. Then, we integrated this framework with multiregional input-output analysis to uncover the multi-scale embodied land-use emissions propelled by Shenzhen's supply-chain activities. Our results indicate a marked increase in Shenzhen's embodied carbon emissions, approximately two orders of magnitude greater than its physical emissions, tripling during 2005-2018. Remarkably, non-agriculture sectors contributed 81.3-90.5 % of physical and 46.6-58.4 % of embodied land-use emissions. The land-use changes occurred outside Shenzhen accounted for 6.5-13.3 % of Shenzhen's total embodied land-use emissions. The sectoral analysis revealed a transition from traditional manufacturing (e.g., metallurgy, chemical products, textiles, wood products) in 2010-2015 to high-tech sectors (e.g., electronic equipment and other manufacture) in 2015-2018. This shift was primarily attributed to concurrent industry transfer actions, leading to aggressive changes in land-use emission intensity discrepancies within and outside Shenzhen. This study provides a scientific basis for designing effective strategies to mitigate land-use carbon emissions associated with supply-chain activities.

Silvopastoral management for lowering trade-offs between beef production and carbon storage in tropical dry woodlands

Tropical dry woodlands and savannas harbour high levels of biodiversity and carbon, but are also important regions for agricultural production. This generates trade-offs between agriculture and the environment, as agricultural expansion and intensification typically involve the removal of natural woody vegetation. Cattle ranching is an expanding land use in many of these regions, but how different forms of ranching mediate the production/environment trade-off remains weakly understood. Here, we focus on the Argentine Chaco, to evaluate trade-offs between beef production and carbon storage in grazing systems with different levels of woody cover (n = 27). We measured beef productivity and carbon storage during 2018/19 and used a regression framework to quantify the trade-off between both, and to analyze which agroclimatic and management variables explain the observed trade-off. Our main finding was that silvopastures had the lowest trade-off between beef production and carbon storage, as management in these systems seeks to increase herbaceous forage by removing shrubs, while maintaining most of the bigger trees that contain most above-ground carbon. The most important variable explaining the beef production/carbon storage trade-off was pasture management, specifically the number of shrub encroachment control interventions, with a lower trade-off for higher numbers of interventions. Unfortunately, more interventions can also result in woody cover degradation over time, and shrub encroachment management must therefore be improved to become sustainable. Overall, our study highlights the strong environmental trade-offs associated with beef production in dry woodlands and savanna, but also the key role of good management practices in lowering this trade-off. Specifically, silvopastoral systems can increase beef production as much as converting woodlands to tree-less pastures, but silvopastures retain much more carbon in aboveground vegetation. Silvopastoral systems thus represent a promising land-use option to lower production/environment trade-offs in the Dry Chaco and likely many other tropical dry woodlands and savannas.

Analyzing long-term dynamics of agricultural greenhouse gas emissions in Austria, 1830-2018

Agriculture is an important contributor to greenhouse gas (GHG) emissions. While the development of agricultural GHG emissions on national and global scales is well studied for the last three to six decades, little is known about their trajectory and drivers over longer periods. In this article, we address this research gap by calculating and analyzing GHG emissions related to agriculture in Austria from 1830 to 2018. We calculate territorial emissions on an annual basis and include all GHG emissions from the processes directly involved in agricultural production. Based on this time series, we quantify the relative importance of major drivers of changes in GHG emissions across time and agricultural product categories, applying a structural decomposition analysis. We find that agricultural GHG emissions in Austria increased by 69 % over the total study period, from 4.6 Mt. CO2e/yr in 1830 to 7.7 Mt. CO2e/yr in 2018. While emissions increased only moderately from 1830 to 1945 (+22 % overall), with strong fluctuations between 1914 and 1945, they doubled from 1945 to 1985. In the most recent period from 1985 to 2018, emissions fell by one third, with decreases leveling off over time. Our decomposition analysis reveals that increases in agricultural production per capita most importantly contributed to the high growth in GHG emissions from 1945 to 1985. Conversely, decreasing emission intensities of products and a more climate friendly product mix were key drivers in the emissions reduction observed after 1985. We also contribute to the discussion around the global warming potential star (GWP*), by calculating GHG emissions based on this alternative metric, and contextualize our data within total socio-economic GHG emission trends. By providing insights into the historical trends and drivers of agricultural GHG emissions, our findings enhance the understanding of their long-term historical dynamics and adds to the knowledge base for future mitigation efforts.

Spatiotemporal analysis of national carbon emission and regional carbon simulation in China

Land use and land cover (LULC) will cause large flows of carbon sources and sinks. As the world's largest carbon emitter with a complicated LULC, China's carbon emissions have profound implications for its ecological environment and future development. In this paper, we account for the land-use changes and carbon emissions of 30 Chinese provinces and cities in China from 2000 to 2020. Furthermore, the spatial correlation of carbon emissions among the study areas is explored. Four typical regions with spatial association (Beijing, Hebei, Sichuan, and Anhui) are selected, and their land-use change trends in 2025 and 2030 are simulated to predict the total carbon emissions in the future. The results show that the distribution of land-use in China is mainly cultivated and woodland, but the growth of urban built-up (UBL) land area indirectly leads to the continuous increase of carbon emissions. Total carbon emissions have increased over the past two decades, albeit at a slower growth rate, with some provinces experiencing no further growth. In the typical regional carbon emission simulation, it is found that the carbon emissions of the four provinces would show a downward trend in the future. The main reason is the reduction in indirect carbon emissions from fossil energy in UBL, while the other part is the influx of carbon sinks due to grassland, woodland, etc. We recommended that future carbon reduction measures should focus and prioritize controlling fossil energy and mitigating carbon emissions from UBL. Simultaneously, the significant contribution of forests and other land types as carbon sinks should be acknowledged to better implement China's carbon neutral commitment.

Effect of agricultural fiscal expenditures on agricultural carbon intensity in China

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry.

High-carbon expansion or low-carbon intensive and mixed land-use? Recent observations from megacities in developing countries: A case study of Shanghai, China

Cities have become significant sources of greenhouse gas emissions. Effective land management may be the solution to carbon neutrality targets for megacities with limited land resources. This paper takes Shanghai as a case study to investigate the regional land use dynamics and its impact on carbon emissions following the implementation of land conservation and intensive use policy. During 2010-2020, the land use pattern in Shanghai changed from the previous urban land expansion to a combination of industrial land reduction and woodland expansion. Meanwhile, the area proportion of land-use mixture grids increased from 90.50% to 92.28% with the spatial pattern of mixed types also changing. Furthermore, the notable land-use mixture does not necessarily lead to carbon emission reduction, but it can reduce carbon emission hotspots in industrial agglomerations by promoting the mixed use of industrial land and other land use types. However, megacities cannot achieve carbon balance through land use management alone. Due to the increasing carbon emission density of hybrid industrial land, the joint implementation of a land conservation and intensive use strategy with industrial and energy structure adjustments may be an effective way forward.

The global mismatch between equitable carbon dioxide removal liability and capacity

Limiting climate change to 1.5°C and achieving net-zero emissions would entail substantial carbon dioxide removal (CDR) from the atmosphere by the mid-century, but how much CDR is needed at country level over time is unclear. The purpose of this paper is to provide a detailed description of when and how much CDR is required at country level in order to achieve 1.5°C and how much CDR countries can carry out domestically. We allocate global CDR pathways among 170 countries according to 6 equity principles and assess these allocations with respect to countries' biophysical and geophysical capacity to deploy CDR. Allocating global CDR to countries based on these principles suggests that CDR will, on average, represent ∼4% of nations' total emissions in 2030, rising to ∼17% in 2040. Moreover, equitable allocations of CDR, in many cases, exceed implied land and carbon storage capacities. We estimate ∼15% of countries (25) would have insufficient land to contribute an equitable share of global CDR, and ∼40% of countries (71) would have insufficient geological storage capacity. Unless more diverse CDR technologies are developed, the mismatch between CDR liabilities and land-based CDR capacities will lead to global demand for six GtCO2 carbon credits from 2020 to 2050. This demonstrates an imperative demand for international carbon trading of CDR.

Environmental tipping points for global soil carbon fixation microorganisms

Carbon fixation microorganisms (CFMs) are important components of the soil carbon cycle. However, the global distribution of CFMs and whether they will exceed the environmental tipping points remain unclear. According to the machine learning models, total carbon content, nitrogen fertilizer, and precipitation play dominant roles in CFM abundance. Obvious stimulation and inhibition effects on CFM abundance only happened at low levels of total carbon and precipitation, where the tipping points were 6.1 g·kg-1 and 22.38 mm, respectively. The abundance of CFMs in response to nitrogen fertilizer changed from positive to negative (tipping point at 9.45 kg ha-1·y-1). Approximately 46% of CFM abundance decline happened in cropland at 2100. Our work presents the distribution of carbon-fixing microorganisms on a global scale and then points out the sensitive areas with significant abundance changes. The previously described information will provide references for future soil quality prediction and policy decision-making.

Agricultural carbon emissions in Zhejiang Province, China (2001-2020): changing trends, influencing factors, and has it achieved synergy with food security and economic development?

Given the huge carbon footprint of agricultural activities, reduction in agricultural carbon emission (ACE) is important to achieve China's carbon peaking and carbon neutrality goals, but it may affect agricultural food security and economic development. Therefore, it is important for scientific carbon reduction measures to understand the multi-year trends and the influencing factors of ACE, and clarify whether the process of ACE affects food security and economic development. This study analyzed the trends of total ACE and ACE caused by different agricultural carbon sources (ACS) from 2001 to 2020 in Zhejiang Province, then we revealed the main influencing factors of ACE based on the logarithmic mean Divisia index (LMDI) model and dissected the relationship between ACE and food security and economic development. Results show that the total ACE fluctuated from 6.10 Mt in 2001 to 3.93 Mt in 2020, and the process included a decrease in 2001-2003 and 2005-2020 and an increase in 2003-2005. The decrease in ACE, from 2001 to 2014, was mainly due to the decline in rice acreage, which contributed 90.38%; from 2014 to 2020, it was by the reduction in the use of fertilizer, diesel, and pesticide, which contributed 83.9%. As drivers, agricultural economic development effect and total population size effect drove 4.25 and 1.54 Mt of ACE, respectively. As inhibitors, planting structure effect, technology development effect, and population structure effect inhibited 3.12, 2.11, and 2.74 Mt of ACE, respectively. With the reduction of ACE, the agricultural economy continued to grow, but the food security situation was pessimistic, indicating that ACE reduction has achieved synergy with economic development, but not with food security.

Agricultural transformation towards delivering deep carbon cuts in China's arid inland areas

Since agriculture is a main source of global greenhouse gas (GHG) emissions, reducing agricultural GHG emissions is crucial for achieving global climate goals. Nevertheless, there has been a lack of thorough and systematic assessment of the spatiotemporal distribution of agricultural GHG emissions at the county level, considering many factors such as crop and livestock products, different processes and gases, and the impact of carbon fixation. Furthermore, the potential of comprehensive technical strategies to reduce GHG emissions remains uncertain. Considering the unique attributes of agricultural development in arid areas of northwest China, this study aimed to explore long-term changes in agricultural net GHG emissions by county, product group, process, and gas and quantify the future reduction potential based on the Agricultural System-induced GreenHouse Gases INVentory (ASGHG-INV) econometric model. The results showed increasing trends in carbon emissions (CE), carbon sequestration (CS), carbon footprint (CF), crop carbon footprint per unit area (CFCF), and crop carbon footprint per unit product (CPCF) in various regions from 1991 to 2019, while there was a decreasing trend in livestock carbon footprint per unit product (LPCF). Focus on reducing GHG emissions in the crop-sector should be in Shihezi, Alaer, and Liangzhou; those of the livestock-sector should be in Xinyuan, Yecheng, Liangzhou, and Gaotai. Scenario analysis indicated that agricultural transformation could substantially reduce GHG emissions in all regions. Reducing the loss of reactive nitrogen was shown to be the most effective single strategy for reducing crop emissions. A comprehensive scheme further integrating the optimization of nitrogen fertilizer management, increasing water-saving, manure application, and straw returning measures, and using biochar and inhibitors can decrease CE, CF, CFCF, and CPCF by 22.62-43.45%, 40.55-111.60%, 41.38-111.78%, and 43.33-111.32%, respectively, increase CS by 9.07-39.97%. Optimizing forage composition was the most influential strategy for reducing livestock GHG emissions. The integrated strategy of further using forage additives, breeding low-emission varieties, and optimizing fecal management can reduce CF and LPCF by 37.32-76.42% and 40.51-78.70%, respectively. This study's results can be a reference for developing more effective GHG emissions reduction and green transformation pathways for global dryland agriculture.

Agricultural Methane Emissions in China: Inventories, Driving Forces and Mitigation Strategies

Identification of the spatial distribution, driving forces, and future trends of agricultural methane (AGM) emissions is necessary to develop differentiated emission control pathways and achieve carbon neutrality by 2060 in China, which is the largest emitter of AGM. However, such research is currently lacking. Here, we estimated China's AGM emissions from 2010 to 2020 and then decomposed six factors that affect AGM emissions via the LMDI model. The results indicated that the AGM emissions in China in 2020 were 23.39 Tg, with enteric fermentation being the largest source, accounting for 43.9% of the total emissions. A total of 39.3% of the AGM emissions were from western China. The main driver of AGM emission reduction was emission intensity, accounting for 59% and 33.7% of methane emission reduction in the livestock sector and rice cultivation, respectively. Additionally, higher levels of urbanization contributed to AGM emission reductions, accounting for 31.3% and 43.0% of the livestock sector and rice cultivation emission reductions, respectively. Based on the SSP-RCP scenarios, we found that China's AGM emissions in 2060 were reduced by approximately 90% through a combination of technology measures, behavioral changes, and innovation policies. Our study provides a scientific basis for optimizing existing AGM emission reduction policies not only in China but also potentially in other high AGM-emitting countries, such as India and Brazil.

Machine Learning as a Strategic Tool for Helping Cocoa Farmers in Côte D'Ivoire

Machine learning can be used for social good. The employment of artificial intelligence in smart agriculture has many benefits for the environment: it helps small farmers (at a local scale) and policymakers and cooperatives (at regional scale) to take valid and coordinated countermeasures to combat climate change. This article discusses how artificial intelligence in agriculture can help to reduce costs, especially in developing countries such as Côte d'Ivoire, employing only low-cost or open-source tools, from hardware to software and open data. We developed machine learning models for two tasks: the first is improving agricultural farming cultivation, and the second is water management. For the first task, we used deep neural networks (YOLOv5m) to detect healthy plants and pods of cocoa and damaged ones only using mobile phone images. The results confirm it is possible to distinguish well the healthy from damaged ones. For actions at a larger scale, the second task proposes the analysis of remote sensors, coming from the GRACE NASA Mission and ERA5, produced by the Copernicus climate change service. A new deep neural network architecture (CIWA-net) is proposed with a U-Net-like architecture, aiming to forecast the total water storage anomalies. The model quality is compared to a vanilla convolutional neural network.

Land resource management patterns and urban air quality-evidence from the "land for development" model with Chinese characteristics

Based on panel data of 282 prefecture-level and above cities in China from 2013 to 2020, this paper investigates the impact and transmission paths of the "LFD" land disposal model on urban air quality at the theoretical and empirical levels using dynamic fixed-effects and dynamic spatial Durbin models. The results show that the way land is allocated in a city has a lagging and long-term impact on air quality not only locally but also in neighboring cities. The type of land supply by local governments to different sectors is an important pathway to influence urban air quality. Extended analysis shows that land market reforms in China can significantly reduce urban air quality index (AQI) and effectively mitigate urban air quality, with long-term effects. This paper provides a theoretical and scientific basis for correcting the mismatch of land resources and promoting urban ecological environment in China.

Sustainable irrigation and climate feedbacks

Agricultural irrigation induces greenhouse gas emissions directly from soils or indirectly through the use of energy or construction of dams and irrigation infrastructure, while climate change affects irrigation demand, water availability and the greenhouse gas intensity of irrigation energy. Here, we present a scoping review to elaborate on these irrigation-climate linkages by synthesizing knowledge across different fields, emphasizing the growing role climate change may have in driving future irrigation expansion and reinforcing some of the positive feedbacks. This Review underscores the urgent need to promote and adopt sustainable irrigation, especially in regions dominated by strong, positive feedbacks.

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Effects of conversion of coastal marshes to aquaculture ponds on sediment anaerobic CO2 production and emission in a subtropical estuary of China

The extensive conversion of carbon-rich coastal wetland to aquaculture ponds in the Asian Pacific region has caused significant changes to the sediment properties and carbon cycling. Using field sampling and incubation experiments, the sediment anaerobic CO₂ production and CO₂ emission flux were compared between a brackish marsh and the nearby constructed aquaculture ponds in the Min River Estuary in southeastern China over a three-year period. Marsh sediment had a higher total carbon and lower C:N ratio than aquaculture pond sediment, suggesting the importance of marsh vegetation in supplying labile organic carbon to the sediment. Conversion to aquaculture ponds significantly decreased sediment anaerobic CO₂ production rates by 69.2% compared to the brackish marsh, but increased CO₂ emission, turning the CO₂ sink (-490.8 ± 42.0 mg m^-2 h^-1 in brackish marsh) into a source (6.2 ± 3.9 mg m^-2 h^-1 in aquaculture pond). Clipping the marsh vegetation resulted in the highest CO₂ emission flux (382.6 ± 46.7 mg m^-2 h^-1), highlighting the critical role of marsh vegetation in capturing and sequestering carbon. Sediment anaerobic CO₂ production and CO₂ uptake (in brackish marsh) and emission (in aquaculture ponds) were highest in the summer, followed by autumn, spring and winter. Redundancy analysis and structural equation modeling showed that the changes of sediment temperature, salinity and total carbon content accounted for more than 50% of the variance in CO₂ production and emission. Overall, the results indicate that vegetation clearing was the main cause of change in CO₂ production and emission in the land conversion, and marsh replantation should be a primary strategy to mitigate the climate impact of the aquaculture sector.

Giant panda-focused conservation has limited value in maintaining biodiversity and carbon sequestration

Biodiversity and climate are interconnected through carbon. Drivers of climate change and biodiversity loss interact in complex ways to produce outcomes that may be synergistic, and biodiversity loss and climate change reinforce each other. Prioritizing the conservation of flagship and umbrella species is often used as a surrogate strategy for broader conservation goals, but it is unclear whether these efforts truly benefit biodiversity and carbon stocks. Conservation of the giant panda offers a paradigm to test these assumptions. Here, using the benchmark estimates of ecosystem carbon stocks and species richness, we investigated the relationships among the giant panda, biodiversity, and carbon stocks and assessed the implications of giant panda conservation for biodiversity and carbon-focused conservation efforts. We found that giant panda density and species richness were significantly positively correlated, while no correlation was found between giant panda density and soil carbon or total carbon density. The established nature reserves protect 26 % of the giant panda conservation region, but these areas contain <21 % of the ranges of other species and <21 % of total carbon stocks. More seriously, giant panda habitats are still facing high risks of habitat fragmentation. Habitat fragmentation is negatively correlated with giant panda density, species richness, and total carbon density. The ongoing giant panda habitat fragmentation is likely to cause an additional 12.24 Tg C of carbon emissions over 30 years. Thus, giant panda-focused conservation efforts have effectively prevented giant panda extinction but have been less effective in maintaining biodiversity and high‑carbon ecosystems. It is urgent for China to contribute to the development of an effective and representative national park system that integrates climate change issues into national biodiversity strategies and vice versa in dealing with the dual environmental challenges of biodiversity loss and climate change under a post-2020 framework.

Changes in global food consumption increase GHG emissions despite efficiency gains along global supply chains

Greenhouse gas (GHG) emissions related to food consumption complement production-based or territorial accounts by capturing carbon leaked through trade. Here we evaluate global consumption-based food emissions between 2000 and 2019 and underlying drivers using a physical trade flow approach and structural decomposition analysis. In 2019, emissions throughout global food supply chains reached 30 ±9% of anthropogenic GHG emissions, largely triggered by beef and dairy consumption in rapidly developing countries-while per capita emissions in developed countries with a high percentage of animal-based food declined. Emissions outsourced through international food trade dominated by beef and oil crops increased by ~1 Gt CO₂ equivalent, mainly driven by increased imports by developing countries. Population growth and per capita demand increase were key drivers to the global emissions increase (+30% and +19%, respectively) while decreasing emissions intensity from land-use activities was the major factor to offset emissions growth (-39%). Climate change mitigation may depend on incentivizing consumer and producer choices to reduce emissions-intensive food products.

Land use and land cover changes implications on biodiversity in the Owabi catchment of Atwima Nwabiagya North District, Ghana

This paper examined land use and land cover (LULC) change and implications to biodiversity in the Owabi catchment of Atwima Nwabiagya North District in Ghana from 1991 to 2021 using remote sensing, and geographic information systems (GIS), with participatory methods such as interviews and questionnaires with a sample size of 200 participants. The use of supervised classification with maximum likelihood algorithm in QGIS was employed to generate LULC maps of 1991, 2001, 2011, and 2021. Molusce Plugin in QGIS was applied to predict probabilities of LULC changes in 10 years (2021-2031). The results showed that high-density forest has disappeared from 1991 to 2021 while built-up has increased and remained the most dominant LULC from 2011 to 2021. There is a continual decline in the number of plant and animal species in and around the Owabi catchment. This can be attributed to the decline of high-density forests and increased built-up in the study area through human actions. The study identified the influence of human activities as the key forces of LULC change to biodiversity loss. This problem stemmed from the taste for housing and trading activities in the Kumasi Metropolitan Area which has resulted in an increasing demand for settlement because of its closeness to Kumasi and its environs. The study recommends that stringent preventive measures should be developed and enforced by various stakeholders including the Forestry Commission, Ghana Water Company Limited, Environmental Protection Agency, as well as the District/Municipal Assemblies to safeguard the forest from human activities. This recommendation will help these agencies to keep abreast with changes in LULC in the various communities and factors such as changes during the planning of the communities.

The effects of urban land use on energy-related CO2 emissions in China

Land use change caused by urbanization is widely believed to be the primary way human activities affect energy use and, thus, CO₂ emissions (CEs) in China. However, there is a limited understanding of the role of land use with detailed categories in energy-related CEs is still absent. This paper aims to narrow the knowledge gap using multi-dimension metrics, including land use scale, mixture, and intensity. These metrics were derived from three years of sequential POI data. A GWR analysis was carried out to examine the associations between land use change and energy-related CEs. Our results show that (1) the scale of most land use types exerted a bidirectional effect on CEs, demonstrating apparent spatiotemporal heterogeneity; (2) land use mixture of mature city agglomerations had a significant suppressive effect on CEs, suggesting mixed land use be advocated in the urbanization process; (3) Land use intensity had a bi-directional association with CEs in most cities, but its adverse effect gradually spread from the west to the northeast. Therefore, systematically regulating land transaction to control land scale, appropriately interplanting biofuel plants, and utilizing renewable energy are encouraged to reduce energy footprints and mitigate CEs in China. The findings and conclusions of this paper enhance our knowledge on the relationship between land use and CEs and present the scientific basis for policy-making in building low-carbon cities in the context of rapidly urbanizing China.

Spatiotemporal characteristics and determinants of agricultural carbon offset rate in China based on the geographic detector

This paper attempts to explore the spatiotemporal variation characteristics of the agricultural carbon offset rate (ACOR) and the reasons that shape its differentiation characteristics in China. To achieve this objective, the Dagum Gini coefficient, kernel density estimation, and geographic detector model are employed in this study. The results show that there are some differences in ACOR among regions in China. Interregional differences are the main source of their overall variation. Excluding the spatial conditions, the ACOR of each province in the sample period shows low mobility characteristics. Considering the spatial conditions, there is convergence in the lower-middle neighborhoods. The three-year lag period did not significantly affect the interaction of ACOR between regions under the accession time horizon. At the aggregate level, the spatial and temporal divergence in China's ACOR is driven by urbanization rate, agricultural fiscal expenditure, and rural education level. As for the regional level, the scale of household farmland operation plays a greater role in determining the spatiotemporal variation of the eastern and central regions' ACOR. While urbanization rate is more determinant for the western region, the interaction between any two factors has significantly higher explanatory power for the spatial and temporal variation of ACOR than the single factor.

National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1850

Anthropogenic emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have made significant contributions to global warming since the pre-industrial period and are therefore targeted in international climate policy. There is substantial interest in tracking and apportioning national contributions to climate change and informing equitable commitments to decarbonisation. Here, we introduce a new dataset of national contributions to global warming caused by historical emissions of carbon dioxide, methane, and nitrous oxide during the years 1851–2021, which are consistent with the latest findings of the IPCC. We calculate the global mean surface temperature response to historical emissions of the three gases, including recent refinements which account for the short atmospheric lifetime of CH4. We report national contributions to global warming resulting from emissions of each gas, including a disaggregation to fossil and land use sectors. This dataset will be updated annually as national emissions datasets are updated.

Effects of different land use patterns on soil properties and N2O emissions on a semi-arid Loess Plateau of Central Gansu

Nitrous oxide (N2O) is one of the significant greenhouse gases in the atmosphere. Different land use patterns are the sink or source of N2O, which plays a vigorous role in controlling N2O emissions. Yet, how different land use patterns affect soil N2O emissions in the Loess Plateau of Central Gansu is still not clear. Therefore; in order to fill this gap, six different land use patterns, including Picea asperata (PA), Hippophae rhamnoides (HR), Medicago sativa (MS), No-tillage wheat field (NT) and Conventional tillage wheat field (T) were studied. The objective of this study was to examine the impact of different land use patterns on soil properties and N2O emission flux. Our results showed that compared with other treatments, Picea asperata woodland increased the soil bulk density, organic matter and soil water content, total nitrogen accumulation and microbial biomass nitrogen whilst reduced the soil pH. The wheat field is more favorable to accumulating soil nitrate nitrogen and ammonium nitrogen. Moreover, soil N2O emission rates followed the trend of T&gt;NT&gt;HR&gt;GL&gt;MS&gt;PA. In addition, soil physicochemical properties were closely related to N2O emission flux and soil temperature was the most significant factor affecting N2O emission. General, Picea asperata woodland could significantly increased soil nutrient and reduce N2O emissions. We suggest that more forest land should be selected as the optimal site for nitrogen fixation and emission reduction for sustainable development of the terrestrial ecosystem on the Loess Plateau in Central Gansu.

Effects of acidification on nitrification and associated nitrous oxide emission in estuarine and coastal waters

In the context of an increasing atmospheric carbon dioxide (CO₂) level, acidification of estuarine and coastal waters is greatly exacerbated by land-derived nutrient inputs, coastal upwelling, and complex biogeochemical processes. A deeper understanding of how nitrifiers respond to intensifying acidification is thus crucial to predict the response of estuarine and coastal ecosystems and their contribution to global climate change. Here, we show that acidification can significantly decrease nitrification rate but stimulate generation of byproduct nitrous oxide (N₂O) in estuarine and coastal waters. By varying CO₂ concentration and pH independently, an expected beneficial effect of elevated CO₂ on activity of nitrifiers ("CO₂-fertilization" effect) is excluded under acidification. Metatranscriptome data further demonstrate that nitrifiers could significantly up-regulate gene expressions associated with intracellular pH homeostasis to cope with acidification stress. This study highlights the molecular underpinnings of acidification effects on nitrification and associated greenhouse gas N₂O emission, and helps predict the response and evolution of estuarine and coastal ecosystems under climate change and human activities.

The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry

The development of green technology (GT) may have a vital influence in decreasing carbon releases, and the linkage between the advancement of GT and CO2 releases in China's agricultural industry has not attracted enough attention. The main objectives of this study are to assess the influence of agricultural green technology advancement on efficiency enhancement, release control capabilities, agricultural energy structure, and agriculture industrial structure. This article decomposes the advancement of green technology (AGTP) in the agricultural industry in China into resource-saving green technology advancement (AEGTP) and emission reduction green technology advancement (ACGTP). At the same time, to evaluate the intermediary impact of green technology advancement, a two-step econometric model and an intermediary impact model were utilized to evaluate the panel data of 30 provinces in China from 1998 to 2018. The role of AGTP (including ACGTP and AEGTP) and CO2 release concentration has also been explored critically. The results show that (i) under the two-step measurement method, AGTP has substantial favorable impacts on agricultural energy efficiency (EF) and possesses a negative impact on agriculture industrial structure (PS) and agricultural energy structure (ES). Agricultural energy efficiency (EF) and agriculture industrial structure (PS) under AGTP will reduce CO2 release concentration, but the path of agricultural energy structure (ES) will increase CO2 release concentration. (ii) At the national level, AGTP has an immediate unfavorable influence on CO2 releases. After introducing the intermediary variables, the intermediary impact of AGTP on CO2 releases through agricultural energy efficiency (EF), agriculture industrial structure (PS), and agricultural energy structure (ES) is also significantly negative, and the direct impacts of each variable are higher than the intermediary impact. (iii) In terms of different zones, the direct impacts of AGTP are all significant. The order of significance of the direct impacts of different zones is west to central and central to eastern. The overall significance ranking of the mediating impact is ACGTP &gt; AEGTP &gt; AGTP, and the significance ranking of each index is ES &gt; EF &gt; PS. Finally, this article puts forward some policy recommendations to reduce CO2 releases.

Cropland displacement contributed 60% of the increase in carbon emissions of grain transport in China over 1990-2015

Rapid urbanization and population growth have increased the need for grain transportation in China, as more grain is being consumed and croplands have been moved away from cities. Increased grain transportation has, in turn, led to higher energy consumption and carbon emissions. Here we undertook a model-based approach to estimate the carbon emissions associated with grain transportation in the country between 1990 and 2015. We found that emissions more than tripled, from 5.68 million tons of CO₂ emission equivalent in 1990 to 17.69 million tons in 2015. Grain production displacement contributed more than 60% of the increase in carbon emissions associated with grain transport over the study period, whereas changes in grain consumption and population growth contributed 31.7% and 16.6%, respectively. Infrastructure development, such as newly built highways and railways in western China, helped offset 0.54 million tons of CO₂ emission equivalent from grain transport. These findings shed light on the life cycle environmental impact within food supply chains.

Deciphering nitrous oxide emissions from tropical soils of different land uses

Tropical regions are hotspots of increasing greenhouse gas emissions associated with land-use change. Although many field studies have quantified soil fluxes of nitrous oxide (N₂O; a potent greenhouse gas) from various land uses, the driving mechanisms remain uncertain. Here, we used tropical soils of diverse land uses and actively manipulated the soil moisture (35%, 60%, and 95% water-filled pore space [WFPS]) and substrate supply (control, nitrate, and nitrate plus glucose) to investigate the responses of N₂O emissions with short-term incubations. We then identified key factors regulating N₂O emissions out of a series of soil physicochemical and biological factors and explored how these factors interacted to drive N₂O emissions. Land-use changes from primary forest to oil palm or Acacia plantation risks emitting more N₂O, whereas low emissions could be maintained by conversion to Macaranga forest or Imperata grassland; these laboratory observations were corroborated by a literature synthesis of field N₂O measurements across tropical regions. Soil redox potential (Eh) and labile organic nitrogen (LON; amino acid mixture, arginine, and urea) mineralization were among the factors with greatest influence on N₂O emissions. In contrast to common understandings, the control of WFPS over N₂O emissions was largely indirect, and acted through Eh. The mineralization of LON, particularly arginine, potentially played multiple roles in N₂O production (e.g., bottlenecks of nitrifier-denitrification or simultaneous nitrification-denitrification versus substrate competition for co-denitrification). Structural equation models suggest that soil-environmental factors of different levels (from distal including land use, soil moisture, and pH to proximal such as LON mineralization) drive N₂O emissions through cascading interactions. Overall, we show that, despite identical initial soil conditions, land conversion can substantially alter the N₂O emission potential. Also, collectively considering soil-environmental regulators and their interactions associated with land conversion is crucial to predict and design mitigation strategies for N₂O emissions from land-use change.

The impact of irrigation modes on agricultural water-energy‑carbon nexus

Despite the tremendous contribution of irrigated agriculture in addressing global food security, there is still confusion for farmers and governments about the choice of irrigation mode owing to the drastic environmental impacts of irrigation, including water shortage, energy crisis, and global warming. Exploring the agricultural water-energy‑carbon (WEC) nexus under different irrigation modes helps to accomplish the multi-objective of water & energy saving and carbon emission reduction. In this paper, a conceptual framework was nominated to evaluate the water & energy consumption and carbon emissions for winter wheat irrigation at township level and quantitatively discuss the complex interaction by the coupling coordination degree (CCD) of the WEC system under different irrigation modes in Henan Province, China. We discovered that irrigation modes profoundly affect water and energy consumption and carbon emissions in agriculture, as well as the spatial distribution of CCD from WEC system. Townships under irrigation mode with diversion and irrigation projects as the primary method (WDI) clustered together in the north and east with highest water consumption and carbon emissions, while townships under irrigation mode with rain-fed agriculture as the primary method (PI) accumulated in the west and south with lower water consumption and carbon emissions. Meanwhile, the CCD of the WEC nexus system was in basic coordination (0.40) and showed an unbalanced spatial distribution pattern with high in the southeast and low in the northwest. By comparing four irrigation modes, the coupling level of the WEC nexus system under irrigation mode with groundwater irrigation as the primary method (GI) was better and PI mode was the least ideal. This study helps to further understand agricultural WEC nexus under different irrigation modes and provide references for local governments in selecting appropriate irrigation modes to realize water-energy saving and carbon emission reduction in agricultural activities.

Meta-analysis of global soil data identifies robust indicators for short-term changes in soil organic carbon stock following land use change

The restoration of degraded lands and minimizing the degradation of productive lands are at the forefront of many environmental land management schemes around the world. A key indicator of soil productivity is soil organic carbon (SOC), which influences the provision of most soil ecosystem services. A major challenge in direct measurement of changes in SOC stock is that it is difficult to detect within a short timeframe relevant to land managers. In this study, we sought to identify suitable early indicators of changes in SOC stock and their drivers. A meta-analytical approach was used to synthesize global data on the impacts of arable land conversion to other uses on total SOC stock, 12 different SOC fractions and three soil structural properties. The conversion of arable lands to forests and grasslands accounted for 91 % of the available land use change datasets used for the meta-analysis and were mostly from Asia and Europe. Land use change from arable lands led to 50 % (32-68 %) mean increase in both labile (microbial biomass C and particulate organic C - POC) and passive (microaggregate, 53-250 μm diameter; and small macroaggregate, 250-2000 μm diameter) SOC fractions as well as soil structural stability. There was also 37 % (24-50 %) mean increase in total SOC stock in the experimental fields where the various SOC fractions were measured. Only the POC and the organic carbon stored in small macroaggregates had strong correlation with total SOC: our findings reveal these two SOC fractions were predominantly controlled by biomass input to the soil rather than climatic factors and are thus suitable candidate indicators of short-term changes in total SOC stock. Further field studies are recommended to validate the predictive power of the equations we developed in this study and the use of the SOC metrics under different land use change scenarios.

Toward a sustainable environment: nexus between geothermal energy growth and land use change in EU economies

There are many advantages of geothermal energy as an environmentally friendly resource; however, there are quite a several challenges that need to be overcome to completely harness sustainable and renewable energy that is also natural. The primary aim of this study is to examine what influence geothermal energy will have on land use changes among the considered 27 states in the European Union from the time being 1990 to 2021. The study adopts the auto-regressive distributed lag (ARDL); the findings show that geothermal energy growth could be leveraged to achieve remarkable growth in land use change among the 13 European developing economies than among the 14 EU developed economies. On the other hand, results from analysis further show that a remarkable decrease in land use change could be better attained among the 14 EU developed economies that among the 13 EU developing economies as a result of institutional quality. Furthermore, the result suggests that through economic growth, there could be a remarkable increase in land use change among the 14 EU developed economies than among the 13 EU developing economies. It was further revealed by the study that the level of land use change among the 27 EU nations could be remarkably increased, boosting the level of geothermal energy production that will assist in attaining the aims behind the 2030 energy union. This will eventually help in curbing the incidence of climate change and pollution in the environment; the projected calculations are observed to be valid, as confirmed through the chosen three estimators for this research. The chosen estimators are the pooled mean group, mean group, and dynamic fixed effect. The regulations and governors in 27 European Union countries should give priority to using geothermal in their renewable energy mix to reduce the incidence of changes in land structures. Also, an increased level of efficiency and effectiveness should be made to the generation of geothermal energy by state actors and investors to prompt sustainability and attainability with no further depreciation in agricultural and forest natural states.

Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to 2017. Landsat data from 1990 (Thematic mapper [TM]), 2000 and 2010 (Enhanced Thematic Mapper [ETM+]), and 2013 to 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into the classes termed snow, water, barren land, built-up area, forest, and vegetation. The method was built using multitemporal Landsat images and the machine learning techniques Support Vector Machine (SVM), Naive Bayes Tree (NBT) and Kernel Logistic Regression (KLR). According to the results, forest area was decreased from 19,360 km² (26.0%) to 18,784 km² (25.2%) from 1990 to 2010, while forest area was increased from 18,640 km² (25.0%) to 26,765 km² (35.9%) area from 2013 to 2017 due to "One billion tree Project". According to our findings, SVM performed better than KLR and NBT on all three accuracy metrics (recall, precision, and accuracy) and the F1 score was >0.89. The study demonstrated that concurrent reforestation in barren land areas improved methods of sustaining the forest and RS and GIS into everyday forestry organization practices in Khyber Pakhtun Khwa (KPK), Pakistan. The study results were beneficial, especially at the decision-making level for the local or provincial government of KPK and for understanding the global scenario for regional planning.

Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model

Future climate change may have substantial impacts on both water resources and food security in China's black soil region. The Liao River Basin (LRB; 220,000 km²) is representative of the main black soil area, making it ideal for studying climate change effects on black soil. In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the LRB. Actual evapotranspiration (ET_a) values calculated using the Surface Energy Balance System (SEBS) model and city-level corn (Zea mays L.) yields were then used to calibrate the SWAT model. Finally, the SWAT model was modified to accept dynamic CO₂ input and output crop transpiration, soil evaporation, and canopy interception separately to explore the impacts of future climate change on ET related variables and crop water productivity (CWP) in the LRB. Simulation scenario design included 22 General Circulation Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs) scenarios from the latest Coupled Model Intercomparison Project 6 (CMIP6) for two 30-year periods of 2041-2070 and 2071-2100. The predicted results showed a significant (P < 0.05) increase in air temperatures and precipitation in the LRB. In contrast, solar radiation decreased significantly and was most reduced for the SSP3-7.0 scenario. Reference evapotranspiration (ET_o), ET_a, and soil evaporation significantly increased in future scenarios, while canopy interception and crop transpiration showed significant reductions, particularly under the 2071-2100 SSP5-8.5 scenario. Overall, corn yield elevated considerably (P < 0.05) with the largest increase for the SSP5-8.5 scenario during 2071-2100. However, the SSP3-7.0 scenario indicated a significant decline in yield. Future changes in CWP were similar to those for corn yield, with significant increases in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. These findings suggested future climate change may have a positive impact on corn production in the black soil region of the LRB.

Ecological spatial intensive use optimization modeling with framework of cellular automata for coordinating ecological protection and economic development

With the exposure of excessive intensive use of urban and agricultural space, the optimization of intensive use of ecological space provides a new way to coordinate the global problem of spatial conflict between ecological protection and economic development. However, the coupling accuracy of the existing structure-spatial coupling optimization model is low, which cannot provide method support for the optimization of intensive use of ecological space. To solve this problem, we propose a new model of ecological spatial intensive use optimization (ESIUO) based on the non-stationarity of the Markov state transition probability of the dominant ecosystem service functions (DESFs) and their suitability, and with the help of the framework of cellular automata (CA). We took Qionglai City as an empirical study area, and compared the results of this model with those of CA-Markov and CLUE-S models with the same parameters. The results show that: (i) The quantitative structure corresponding to the spatial layout of each dominant ecosystem service function (DESF) optimized by the ESIUO model has the smallest relative error (δ_k≤0.04%) with the optimal quantitative structure. (ii) The layout of DESFs optimized by the ESIUO model maximizes the supply capacity of ecosystem services. The minimum matching degree between the distribution of each DESF and the high-value area of its suitability is 92.06 %, and the spatial distribution is more compact, and the comprehensive effect of spatial layout is the best. Further analysis confirmed that the model can establish the spatial layout of DESFs that can realize the high precision coupling with the optimal quantitative structure of DESFs in terms of quantitative structure, and can support the construction of the layout of intensive use of ecological space to alleviate the pressure of non-ecological space expansion in these areas, and then provide a new way to coordinate ecological protection and economic development.