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

Spatial differentiation identification of influencing factors of agricultural carbon productivity at city level in Taihu lake basin, China

Improving carbon productivity is the main way to deal with climate change under China's targets for carbon emissions to peak by 2030 and carbon neutrality by 2060. This study identified the spatial differentiation of influencing factors of agricultural carbon productivity at the city level in Taihu lake basin, and formed differentiated agricultural management strategies. The results show that: (1) Spatial differentiation of agricultural carbon productivity is obvious at city level. It can be divided into three echelons: the first echelon is Shanghai and Hangzhou (agricultural carbon productivity≥10,000 Yuan/t in 2019 with a growth rate ≥ 600% compared with 1992), the second echelon is Suzhou, Wuxi and Changzhou (9000 Yuan/t ≤ agricultural carbon productivity<10,000 Yuan/t in 2019 with 381% ≤ growth rate < 600% compared with 1992), and the third echelon is Zhenjiang, Huzhou and Jiaxing (agricultural carbon productivity<9000 Yuan/t in 2019 or a growth rate < 381% compared with 1992). (2) There is a synergetic evolution law between agricultural carbon productivity and agricultural economy, that is, agricultural economic development level is the first factor affecting agricultural carbon productivity, whether in the whole basin or in the city level. (3) There are significant differences in the influencing factors of agricultural carbon productivity at the city level. Finally, according to the spatial differentiation characteristics of influencing factors of agricultural carbon productivity at the city level in Taihu lake basin, we put forward different emphases of agricultural development in different cities.

A systematic bibliometric review of clean energy transition: Implications for low-carbon development

More voices are calling for a quicker transition towards clean energy. The exploration and exploitation of clean energy such as wind energy and solar energy are effective means to optimise energy structure and improve energy efficiency. To provide in-depth understanding of clean energy transition, this paper utilises a combination of multiple bibliometric mapping techniques, including HistCite, CiteSpace and R Bibliometrix, to conduct a systematic review on 2,191 clean energy related articles obtained from Web of Science (WoS). We identify five current main research streams in the clean energy field, including Energy Transition, Clean Energy and Carbon Emission Policy, Impact of Oil Price on Alternative Energy Stocks, Clean Energy and Economics, and Venture Capital Investments in Clean Energy. Clearly, the effectiveness of policy-driven and market-driven energy transition is an important ongoing debate. Emerging research topics are also discussed and classified into six areas: Clean Energy Conversion Technology and Biomass Energy Utilisation, Optimisation of Energy Generation Technology, Policy-Making in Clean Energy Transition, Impact of Clean Energy Use and Economic Development on Carbon Emissions, Household Use of Clean Energy, and Clean Energy Stock Markets. Accordingly, more and more research attention has been paid to how to improve energy efficiency through advanced clean energy technology, and how to make targeted policies for clean energy transition and energy market development. This article moves beyond the traditional literature review methods and delineates a systematic research agenda for clean energy research, providing research directions for achieving low-carbon development through the clean energy transition.

The impact of land resource mismatch and land marketization on pollution emissions of industrial enterprises in China

Local governments' strategy of increasing the land supply by selling industrial land at low prices causes industrial land in China to be priced substantially below the market value. Whether under planned or market allocation, urban land is an important part of China's market-oriented economic reform. However, direct empirical research on the impact of industrial land transfer on environmental pollution is still lacking. Based on matched data, this study investigates the impact of land resource mismatch and land marketization on the pollution emissions of Chinese enterprises. Pollution emissions refer to the emission intensity of industrial enterprises' major pollutants, such as industrial water, industrial waste gas and sulfur dioxide, and it is calculated by the comprehensive index method. The impact of land resource mismatch and land marketization on the pollution emissions of Chinese enterprises is investigated using a panel fixed effect model, subsample regression, the instrumental variable method and a mediating effect model. The benchmark analysis shows that land resource mismatch increases pollution emissions. The influence mechanism analysis shows that land resource mismatch may increase the pollution emissions of enterprises by causing overinvestment and low productivity. In addition, the expanded analysis shows that China's land marketization can reduce the intensity of and have a long-term impact on the pollution emissions of enterprises. This paper provides a theoretical and scientific basis for correcting the mismatch of land resources and promoting the reform of land marketization in China.

Atmospheric methane removal: a research agenda

Atmospheric methane removal (e.g. in situ methane oxidation to carbon dioxide) may be needed to offset continued methane release and limit the global warming contribution of this potent greenhouse gas. Because mitigating most anthropogenic emissions of methane is uncertain this century, and sudden methane releases from the Arctic or elsewhere cannot be excluded, technologies for methane removal or oxidation may be required. Carbon dioxide removal has an increasingly well-established research agenda and technological foundation. No similar framework exists for methane removal. We believe that a research agenda for negative methane emissions-'removal' or atmospheric methane oxidation-is needed. We outline some considerations for such an agenda here, including a proposed Methane Removal Model Intercomparison Project (MR-MIP). This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Impact of landscape pattern changes on hydrological ecosystem services in the Beressa watershed of the Blue Nile Basin in Ethiopia

Landscape pattern changes are mostly due to human activities, and such changes often affect ecosystem functions and services. This study was conducted to evaluate the response of hydrological ecosystem services (HESs) to structural landscape changes. Spatiotemporal changes in two specific HES indicators, water yield (WY) and sediment export (SE), were quantified by analyzing historic (1972-2017) and projected land use/land cover changes (2017-2047). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Model was used for this purpose. Results indicated that WY and SE changed significantly (p ˂ 0.01) during the study period. The total WY and SE increased by 30.29% and 98.69%, respectively, between 1972 and 2017. Analysis of the projections for the next three decades (2017-2047) suggested an increase in WY and SE by 4.8% and 93.11%, respectively. Furthermore, results revealed that WY and SE are strongly influenced by landscape composition, and metrics such as percentage of landscape (PLAND), mean patch size (MPS), and large patch index (LPI) of farmland and plantations were found to be key factors affecting HESs degradation in the Beressa watershed. PLAND (VIP = 1.34; w = 0.55; and VIP = 1.32; w = 0.56) and MPS (VIP = 1.32; w = 0.50 and VIP = 1.31; w = 0.56)) of farmland cover contributed most to the changes in WY and SE, respectively. Similarly, PLAND (VIP = 1.33; w = 0.54 and VIP = 1.28; w = 0.52), LPI (VIP = 1.27; w = 0.52 and VIP = 1.30; w = 0.54) and MPS (VIP = 1.29; w = 0.52) of plantation cover also contributed more to the change in WY and SE. Besides that, of anthropogenic factors, compositions of natural vegetation and grassland cover were found to heavily influence HESs in the watershed studied. The findings of the study suggest that soil and water conservation interventions are vital to minimize and control water-related problems and enhance ESs.

Decoupling between ammonia emission and crop production in China due to policy interventions

Cropland ammonia (NH₃ ) emission is a critical driver triggering haze pollution. Many agricultural policies were enforced in past four decades to improve nitrogen (N) use efficiency while maintaining crop yield. Inadvertent reductions of NH₃ emissions, which may be induced by such policies, are not well evaluated. Here, we quantify the China's cropland-NH₃ emission change from 1980 to 2050 and its response to policy interventions, using a data-driven model and a survey-based dataset of the fertilization scheme. Cropland-NH₃ emission in China doubled from 1.93 to 4.02 Tg NH₃ -N in period 1980-1996, and then decreased to 3.50 Tg NH₃ -N in 2017. The prevalence of four agricultural policies may avoid ~3.0 Tg NH₃ -N in 2017, mainly located in highly fertilized areas. Optimization of fertilizer management and food consumption could mitigate three-quarters of NH₃ emission in 2050 and lower NH₃ emission intensity (emission divided by crop production) close to the European Union and the United States. Our findings provide an evidence on the decoupling of cropland-NH₃ from crop production in China and suggest the need to achieve cropland-NH₃  mitigation while sustaining crop yields in other developing economies.

Differential response of soil CO2 , CH4 , and N2 O emissions to edaphic properties and microbial attributes following afforestation in central China

Land use change specially affects greenhouse gas (GHG) emissions, and it can act as a sink/source of GHGs. Alterations in edaphic properties and microbial attributes induced by land use change can individually/interactively contribute to GHG emissions, but how they predictably affect soil CO₂ , CH₄ , and N₂ O emissions remain unclear. Here, we investigated the direct and indirect controls of edaphic properties (i.e., dissolved organic carbon [DOC], soil organic C, total nitrogen, C:N ratio, NH4+ -N, NO3- -N, soil temperature [ST], soil moisture [SM], pH, and bulk density [BD]) and microbial attributes (i.e., total phospholipid fatty acids [PLFAs], 18:1ω7c, nitrifying genes [ammonia-oxidizing archaea, ammonia-oxidizing bacteria], and denitrifying genes [nirS, nirK, and nosZ]) over the annual soil CO₂ , CH₄ , and N₂ O emissions from the woodland, shrubland, and abandoned land in subtropical China. Soil CO₂ and N₂ O emissions were higher in the afforested lands (woodland and shrubland) than in the abandoned land, but the annual cumulative CH₄ uptake did not significantly differ among all land use types. The CO₂ emission was positively associated with microbial activities (e.g., total PLFAs), while the CH₄ uptake was tightly correlated with soil environments (i.e., ST and SM) and chemical properties (i.e., DOC, C:N ratio, and NH4+ -N concentration), but not significantly related to the methanotrophic bacteria (i.e., 18:1ω7c). Whereas, soil N₂ O emission was positively associated with nitrifying genes, but negatively correlated with denitrifying genes especially nosZ. Overall, our results suggested that soil CO₂ and N₂ O emissions were directly dependent on microbial attributes, and soil CH₄ uptake was more directly related to edaphic properties rather than microbial attributes. Thus, different patterns of soil CO₂ , CH₄ , and N₂ O emissions and associated controls following land use change provided novel insights into predicting the effects of afforestation on climate change mitigation outcomes.

Altered growth conditions more than reforestation counteracted forest biomass carbon emissions 1990-2020

Understanding the carbon (C) balance in global forest is key for climate-change mitigation. However, land use and environmental drivers affecting global forest C fluxes remain poorly quantified. Here we show, following a counterfactual modelling approach based on global Forest Resource Assessments, that in 1990-2020 deforestation is the main driver of forest C emissions, partly counteracted by increased forest growth rates under altered conditions: In the hypothetical absence of changes in forest (i) area, (ii) harvest or (iii) burnt area, global forest biomass would reverse from an actual cumulative net C source of c. 0.74 GtC to a net C sink of 26.9, 4.9 and 0.63 GtC, respectively. In contrast, (iv) without growth rate changes, cumulative emissions would be 7.4 GtC, i.e., 10 times higher. Because this sink function may be discontinued in the future due to climate-change, ending deforestation and lowering wood harvest emerge here as key climate-change mitigation strategies.

Wild meat consumption in tropical forests spares a significant carbon footprint from the livestock production sector

Whether sustainable or not, wild meat consumption is a reality for millions of tropical forest dwellers. Yet estimates of spared greenhouse gas (GHG) emissions from consuming wild meat, rather than protein from the livestock sector, have not been quantified. We show that a mean per capita wild meat consumption of 41.7 kg yr⁻¹ for a population of ~ 150,000 residents at 49 Amazonian and Afrotropical forest sites can spare ~ 71 MtCO₂-eq annually under a bovine beef substitution scenario, but only ~ 3 MtCO₂-eq yr⁻¹ if this demand is replaced by poultry. Wild meat offtake by these communities could generate US$3M or US$185K in carbon credit revenues under an optimistic scenario (full compliance with the Paris Agreement by 2030; based on a carbon price of US$50/tCO₂-eq) and US$1M or US$77K under a conservative scenario (conservative carbon price of US$20.81/tCO₂-eq), representing considerable incentives for forest conservation and potential revenues for local communities. However, the wild animal protein consumption of ~ 43% of all consumers in our sample was below the annual minimum per capita rate required to prevent human malnutrition. We argue that managing wild meat consumption can serve the interests of climate change mitigation efforts in REDD + accords through avoided GHG emissions from the livestock sector, but this requires wildlife management that can be defined as verifiably sustainable.

Economic effects of climate change on global agricultural production

Climate change seems to be larger, more complex and more unpredictable than any other environmental problem. This review deals with the economic effects of climate change on global agricultural production. The causes and consequences of climate change are very diverse, while populations in low-income countries are increasingly exposed to its negative effects. Supplying the population with food is possible with increased agricultural production, but this often occurs under unsustainable circumstances. Increased agricultural production is also one of the main sources of greenhouse gas emissions. In this research we highlight some of the important connections between climate change, population growth and agricultural production.

Emerging reporting and verification needs under the Paris Agreement: How can the research community effectively contribute?

Greenhouse gas (GHG) emission inventories represent the link between national and international political actions on climate change, and climate and environmental sciences. Inventory agencies need to include, in national GHG inventories, emission and removal estimates based on scientific data following specific reporting guidance under the United Nation Framework Convention on Climate Change (UNFCCC) and the Paris Agreement, using the methodologies defined in the Intergovernmental Panel on Climate Change (IPCC) Guidelines. Often however, research communities and inventory agencies have approached the problem of climate change from different angles and by using terminologies, metrics, rules and approaches that do not always match. This is particularly true dealing with "Land Use, Land-Use Change and Forestry" (LULUCF), the most challenging among the inventory sectors to deal with, mainly because of high level of complexity of its carbon dynamics and the difficulties in disaggregating the fluxes between those caused by natural and anthropogenic processes. In this paper, we facilitate the understanding by research communities of the current (UNFCCC) and future (under the Paris Agreement) reporting requirements, and we identify the main issues and topics that should be considered when targeting improvement of the GHG inventory. In relation to these topics, we describe where and how the research community can contribute to producing useful inputs, data, methods and solutions for inventory agencies and policy makers, on the basis of available literature. However, a greater effort by both communities is desirable for closer cooperation and collaboration, for data sharing and the understanding of respective and common aims.

Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

Land cover mapping is often performed via satellite or aerial multispectral/hyperspectral datasets. This paper explores new potentials for the characterisation of land cover from archive greyscale satellite sources by using classification analysis of colourised images. In particular, a CORONA satellite image over Larnaca city in Cyprus was used for this study. The DeOldify Deep learning method embedded in the MyHeritage platform was initially applied to colourise the CORONA image. The new image was then compared against the original greyscale image across various quality metric methods. Then, the geometric correction of the CORONA coloured image was performed using common ground control points taken for aerial images. Later a segmentation process of the image was completed, while segments were selected and characterised for training purposes during the classification process. The latest was performed using the support vector machine (SVM) classifier. Five main land cover classes were selected: land, water, salt lake, vegetation, and urban areas. The overall results of the classification process were then evaluated. The results were very promising (>85 classification accuracy, 0.91 kappa coefficient). The outcomes show that this method can be implemented in any archive greyscale satellite or aerial image to characterise preview landscapes. These results are improved compared to other methods, such as using texture filters.

Energy implications of the 21st century agrarian transition

The ongoing agrarian transition from small-holder farming to large-scale commercial agriculture is reshaping systems of production and human well-being in many regions. A fundamental part of this global transition is manifested in large-scale land acquisitions (LSLAs) by agribusinesses. Its energy implications, however, remain poorly understood. Here, we assess the multi-dimensional changes in fossil-fuel-based energy demand resulting from this agrarian transition. We focus on LSLAs by comparing two scenarios of low-input and high-input agricultural practices, exemplifying systems of production in place before and after the agrarian transition. A shift to high-input crop production requires industrial fertilizer application, mechanization of farming practices and irrigation, which increases by ~5 times fossil-fuel-based energy consumption compared to low-input agriculture. Given the high energy and carbon footprints of LSLAs and concerns over local energy access, our analysis highlights the need for an approach that prioritizes local resource access and incorporates energy-intensity analyses in land use governance.