Hybrid incentives for mitigating agricultural emissions in South Africa: Evidence-based strategies for reconciling environmental and economic sustainability

Introduction Food production systems are a significant source of carbon emissions, and optimizing agricultural infrastructure is crucial for advancing low-carbon food systems. This study investigates how different configurations of agricultural infrastructure contribute to lower carbon intensity in 30 Chinese provinces between 2013 and 2022. Methods We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the combined effects of farmland water conservancy, rural transportation, digital infrastructure, agricultural electrification, cultivation scale, and technological progress on agricultural carbon emissions. Results Our findings reveal that no single infrastructure type is necessary for emission reduction. Instead, two dominant pathways emerge. The scale expansion pathway involves the joint presence of farmland water conservancy, rural transportation, and large-scale grain cultivation. The technological progress pathway combines digital infrastructure, farmland water conservancy, and advances in grain production technology. The technological progress pathway demonstrates slightly stronger consistency (0.9813) than the scale expansion pathway (0.9674). Discussion Regional analysis shows that scale-driven pathways are more common in southeastern coastal provinces, whereas technology-driven pathways dominate in northwestern and southwestern regions. These findings provide actionable guidance for region-specific, policy-driven low-carbon transitions in Chinese agriculture.

Revealing the impact of socio-economic variables on agricultural carbon emission intensity using interpretable machine learning

Haryana state, positioned between Punjab and Delhi, is uniquely exposed to both local agricultural residue burning emissions and transboundary pollution transported by prevailing north-westerly winds during the October-November period. Stubble burning smoke carried by wind only reaches Delhi, if any, after crossing the atmosphere above the central part of Haryana. Most of the past studies discussed the air quality issues of Delhi and correlated it with stubble burning in Punjab, but not for Haryana state in detail. Considering this, using validated/calibrated 24 CAAQMS data, the present study analyzes the air quality status of Haryana state during this post-monsoon season for 3 years (2020-2023). The study considered both particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, SO2 and CO). The findings reveal high levels of PM2.5 and PM10 in the whole state, exceeding specified standards during the post-monsoon season. The increment was highest in the Northwest part of Haryana state (area near stubble burnings along Punjab state). The stations aligned with the dominant downwind paths recorded markedly higher PM2.5 and PM10 levels by 32% and 24%, respectively. The gaseous air pollutants also showed an increment comparatively less. The findings stress the need for region-specific, seasonal policies, including in situ residue management, stricter burning bans, and expanded monitoring. Coordination with Punjab and Delhi NCR under airshed management is crucial. The study offers strong evidence for targeted air quality strategies in Haryana.

Agricultural soils generate ~60% of anthropogenic nitrous oxide (N₂O) emissions, primarily through microbial urease activity during urea hydrolysis. With N₂O possessing 265× CO₂'s global warming potential, effective mitigation strategies must balance emission reductions with maintained crop productivity. Urease inhibitors (NBPT/HQ), controlled-release fertilizers, and microbiome modifications show promise, with efficacy varying by soil type (pH, texture), climate, and management practices. Field trials and meta-analyses (2010-2024) demonstrate these approaches can achieve 30-50% N₂O reductions without yield penalties when properly implemented. To evaluate urease inhibition as an N₂O mitigation tool by synthesizing global evidence (2010-2024) and quantifying its efficacy across soil types, crops, and climates. This systematic review (2010-2024) synthesizes data from Web of Science, Scopus, and GRACEnet to evaluate urease inhibition impacts on agricultural N₂O emissions. Incorporating field trials and controlled studies, we employed meta-analytic methods (R metafor package) to quantify effect sizes, assess heterogeneity, and visualize responses. Our interdisciplinary approach analyzed treatment efficacy across soil types and management practices, providing robust evidence for climate-smart mitigation strategies. Urease inhibitors reduced N₂O emissions by 42% (95% CI: 35–50%) vs. conventional urea, with peak efficacy in clay soils (48% reduction) and humid climates. NBPT outperformed HQ (p < 0.01), especially in neutral-pH soils. Crop yields increased marginally (+3.5%), and NUE improved by 18%. However, sandy soils showed lower efficacy (28% reduction). Meta-regression linked inhibitor performance to soil organic carbon (R² = 0.67) and precipitation (R² = 0.53). Urease inhibitors are a robust N₂O mitigation tool, but soil-specific adoption is vital. Integrating them with nitrification inhibitors or biochar could enhance gains. Scaling requires policy incentives and farmer education to address cost and knowledge barriers.

Agriculture generates roughly one-third of global greenhouse gas (GHG) emissions and is the primary driver of biodiversity loss, underscoring the need to assess the environmental impact of all crops. However, the spice sector has received little attention, with only a few life cycle assessment (LCA) studies available. This study evaluates the carbon and biodiversity footprint of the global spice sector and situates the sector within the broader agricultural system. Climate impacts were quantified using the IPCC GWP100a method, while biodiversity impacts were assessed using LC-IMPACT and a land-use-induced biodiversity assessment method (the high-resolution land use intensities and fragmentation method, LUIF). Indirect drivers of species loss were found to be significant, highlighting the importance of biodiversity assessment beyond only land-use-induced impacts. To capture these effects more comprehensively, the LUIF method was integrated into the LC-IMPACT framework. Results indicate that the global spice sector emits between 41.4 and 64.8 Mt CO2-eq annually and causes approximately 7.2 × 10-4 PDF·yr (potentially disappeared fraction of species), including land plants and vertebrates. Although spices account for only 0.18% of agri-food sector turnover and 0.06% of global crop production volume, they contribute 0.33% of agricultural GHG emissions.

ABSTRACT This study examines the socioeconomic determinants of agricultural CO 2 emissions in the 10 highest‐emitting agricultural economies from 1992 to 2022. Using the LM bootstrap cointegration method and the method of moments quantile regression (MMQR), the analysis identifies long‐run relationships and heterogeneous effects across the emissions distribution. The results indicate that agricultural energy use is the most significant driver of CO 2 emissions, with its impact intensifying at higher quantiles, where environmental pressures are more severe. Agricultural value added also increases emissions, showing that productivity gains in these economies remain carbon‐intensive. Additionally, agricultural trade openness and rural population size contribute positively to emissions, reflecting structural constraints in agricultural production systems. These findings directly relate to several Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger) through the need for resilient and low‐carbon food systems, SDG 7 (affordable and clean energy) via the transition to renewable energy use in agriculture, SDG 12 (responsible consumption and production) through improved resource efficiency, and SDG 13 (climate action) by highlighting the urgency of reducing agricultural emissions. The quantile results indicate the need for stricter renewable energy mandates in high‐emission groups, efficiency‐focused technology support in middle groups, and targeted measures to ease trade and population‐driven pressures in lower groups. The findings indicate that improving energy efficiency in agricultural production and implementing environmentally aligned trade regulations can deliver measurable reductions in sectoral carbon intensity, directly supporting SDG 13 while maintaining productivity gains consistent with SDG 2 objectives.

Achieving net-zero greenhouse gas (GHG) emissions in agriculture is a central objective of climate policy frameworks such as the Paris Agreement. This study explored the feasibility and trade-offs of achieving net zero at the farm level by combining life cycle assessment with modeling of soil organic carbon (SOC) stocks. Four case-study farms, two crop and two dairy, in Italy, the United Kingdom (UK), France, and Germany, and 11 mitigation actions were assessed under two 20-year eco-design scenarios: one maintaining ≥90% of baseline productivity (PM), and one achieving net zero. The scenarios combined nature-based solutions (e.g., organic fertilization, cover crops) with technological interventions (e.g., feed additive, solar power). Estimated GHG emissions decreased greatly, but SOC sequestration alone was insufficient to achieve net zero while maintaining productivity. Under the PM scenario, the Italian, French, and German farms still emitted 51%, 62%, and 84% of baseline emissions, respectively. The UK crop farm achieved net zero under the PM scenario, but had the highest ecotoxicity impact per ha, 11% higher than that of the Italian crop farm. Mitigation effectiveness depended on soil- and crop-management practices, baseline GHG emissions, and carbon inputs. Assumptions about the 20-year amortization window, nutrient cycling, and indirect GHG emissions influenced trade-offs between environmental impacts and productivity. Net zero may be pursued more effectively through cooperation among farms at the landscape or sector level. Assessing the entire agricultural value chain, improving model calibration, and supporting long-term transitions through policies will be essential for developing climate mitigation actions adoptable across European agriculture.

Methane (CH4) emission estimates for South Korea using Lagrangian-based inversion modeling (2010–2021)

ABSTRACT Agricultural methane emissions represent a significant contributor to global climate change, with irrigated rice cultivation being one of the primary sources. Despite the availability of effective mitigation technologies, their adoption often remains limited due to behavioural and institutional constraints. Water management practice that extends drainage periods during cultivation can curb methane emissions from irrigated rice at low cost, yet uptake among Japanese farmers remains modest. We surveyed 2219 rice producers in Shiga Prefecture using a labelled choice experiment that embedded two behavioural nudges (social norm and loss aversion) and one informational boost (knowledge enhancement), presented with or without a reminder. Farmers most favoured a 7‐day drainage extension. Social‐norm messages did not robustly shift adoption intentions, but loss‐aversion and knowledge enhancement paired with reminders increased the probability of choosing water management practice by 5–10 percentage points, particularly when financial incentives were modest. The added value of nudges and boosts faded once subsidies approached prevailing ceiling levels, suggesting diminishing marginal returns to stacking instruments. These findings suggest that timely, low‐cost behavioural interventions can effectively complement agri‐environmental payments in resource‐constrained settings, providing a scalable and context‐sensitive strategy to accelerate the adoption of climate‐smart rice practices.

How does digital economy affect the synergistic reduction effect of agricultural pollution and carbon emissions?

A GeoML-XAI framework for identifying high PM2.5 areas and source attribution: Application to an agricultural environment.

Climate-smart agriculture entails the reduction of CO₂ emissions, adaptation and modification of technology to enhance resilience to climate change, and sustainable increase of incomes. This study evaluates the effectiveness of smart agricultural practices in Turkey, which significantly impact food security and mitigate CO₂ emissions. The decoupling technique was implemented to estimate the portfolio returns and examine their correlation with climate-smart agriculture and CO₂ emissions over the anticipated period of 1992–2023. The decoupling technique was implemented to accomplish the two primary objectives. First, it is employed to calculate the percentage change in portfolio returns that is linked to both high and low weighted risk allocations. Second, it enables the prediction of CO₂ emissions levels for the next five years, which are influenced by sustainability practices and food security fluctuations. A corresponding difference in the efficacy of climate-smart agriculture has been demonstrated in the agricultural context by a percentage change in continuous, single aeration, and multiple aeration practices. The estimated results suggest that the decoupling trend in portfolio returns is significantly influenced by factors such as rice cultivation, field rise, and soil management, which also contribute to the highest weighted risk. Additionally, this factor consistently shows the highest weighted importance in determining overall portfolio returns, as it exhibits the largest marginal effects. Consequently, this investigation substantiates the detrimental influence of these variables on CO₂ emissions. Turkey’s sustainable smart agriculture process is essential for the efficient expansion of the economy, as it integrates climate change considerations into current policies and initiatives and reinforces the policy indicator of economic consideration with environmental protection in terms of CO₂ emissions.

This study examines the coupling coordination between energy consumption and carbon emissions in China’s agricultural sector and explores its implications for the low carbon transition. Using a coupling coordination model and provincial panel data from 2012 to 2021, the study assesses the level of energy carbon coordination in China’s agricultural sector and analyzes its key determinants. Fixed effects models evaluate the impact of terminal energy structure, while mediation analysis tests whether agricultural carbon emission intensity transmits these effects. Additionally, out of sample validation and short term scenario projections are used to examine temporal stability. The results show that energy carbon coordination in China’s agricultural sector is generally at a moderate to good level, indicating progress toward green transformation, although substantial regional disparities remain. Terminal energy structure is a key determinant, higher coal and diesel shares significantly reduce coordination, whereas a higher natural gas share improves it. Mediation analysis further shows that agricultural carbon emission intensity partly transmits these effects. Regional heterogeneity is observed, low GDP provinces are mainly constrained by coal dependence, while high GDP provinces face stronger diesel and electricity related emission pressures. The out of sample validation and short term scenario projections provide additional support for the temporal stability of these findings. These findings highlight the need for region specific decarbonization strategies in China’s agricultural sector, including accelerated clean energy substitution, power sector decarbonization, stronger carbon monitoring, and improved cross sector policy coordination.

How does digital technology innovation affect agricultural carbon emissions? — empirical evidence from China

ABSTRACT In a landmark decision from 2024, Denmark became the first country in the world to introduce carbon pricing on agricultural emissions. The ambition of getting to net-zero carbon emissions requires substantial reductions in the agricultural sector. But reducing emissions here is not straightforward. As Danish agriculture is a major industry with substantial exports, it raises the question: what brought about this dramatic and surprising policy change? Based on document analysis and interviews, this article explores explanations from historical institutionalism, political economy and politicization literature. The article finds that adopting long-term and clearly formulated net-zero targets required all sectors to contribute to emission reductions. This changed the political economy, allowing for sectoral cost profiles for climate transitions to be compared, with policy discussions on how to utilize first-mover advantages. Climate policy was high on the governmental policy agenda, and positive politicization created an opportunity for radical climate policy development. Crucially, a green transition of the agricultural sector is seen as a competitive advantage by the sector itself. Findings indicate that carbon pricing in agriculture needs to be part of a larger policy portfolio, with substantial funding and revenue recycling. Key policy insights Adopting a net-zero target changed the political economy of the Danish climate transition. This allowed cost-profiles for climate transitions across sectors to be compared and for debating how to utilize first-mover advantages. Political competition for ambitious climate policy made radical policy development possible. Still, it is crucial that actors utilize the opportunity while it is still open. Carbon pricing for agriculture may be enabled by being part of a larger policy portfolio, with a broad policy mix, substantial funding and revenue recycling. The European Commission has displayed an interest in developing a carbon pricing mechanism for agriculture. Consequently, the EU may look to the Danish example.

Urea volatilization is a major pathway of nitrogen loss and is a source of ammonia emissions in large-scale agriculture. Existing evaluation methods are time-consuming, expensive equipment-dependent, and often rely on empirical correlations without physical meaning. This study introduces a fast and physically grounded approach to quantifying urea volatilization under both laboratory and field conditions. The method integrates simple gravimetric experiments with a mechanistic model that captures the coupled processes of water evaporation, enzymatic hydrolysis, and gas transfer. By estimation of effective parameters directly linked to mass transfer and reaction kinetics, it enables a consistent comparison of fertilizers with distinct mitigation strategies. Results confirm the method’s ability to distinguish barrier, inhibition, and combined effects, showing its reproducibility and physical coherence. This framework provides a practical tool for fast, meaningful evaluation of fertilizer efficiency, contributing to the development of low-emission and sustainable nitrogen management in precision agriculture.

Background Agriculture contributes approximately 17% of China’s greenhouse gas emissions. In 2017, China established Green Finance Reform and Innovation Pilot Zones (GFRIPZ) to promote green development, yet its effectiveness in reducing agricultural carbon emissions remains unclear. Objectives This study examines whether GFRIPZ reduces agricultural carbon emissions, identifies transmission mechanisms, and explores regional heterogeneity in policy effectiveness. Methods Using GFRIPZ establishment as a quasi-natural experiment, we employ a Double/Debiased Machine Learning (DML) framework with provincial panel data from 30 Chinese provinces (2011–2024, N = 420). Three algorithms (Lasso-CV, Elastic Net-CV, Random Forest) ensure robustness. Parallel trend tests, placebo tests (500 iterations), and mediation analysis validate identification and mechanisms. Results GFRIPZ reduces agricultural carbon emissions by approximately 41.2 × 10 4 tons annually (10.9% reduction, p < 0.01). Three transmission mechanisms are identified: input structure optimization (19.8%), green technology innovation (15.6%), and industrial structure adjustment (11.8%), collectively explaining 47.2% of total effect. Heterogeneity analysis reveals stronger effects in eastern regions (β = −54.0, p < 0.01) than central/western regions (β = −31.4, p < 0.10), and in economically developed provinces. Conclusion GFRIPZ effectively reduces agricultural carbon emissions, with input structure optimization as the primary channel. Policy recommendations include expanding pilot coverage, prioritizing fertilizer reduction investments, and strengthening financial infrastructure in less developed regions.

Research on agricultural carbon emissions in China’s major grain-producing regions: assessment, influencing factors, and pathways for emission reduction

The agricultural sector is not only an important component of economic growth, but the environmental degradation caused by the sector has also been a significant research topic in recent years. This study aims to examine the time-frequency dependencies between agricultural activities, economic growth (GDP), and environmental degradation during the period 1961-2021. Instead of traditional time series analyses, the Cross-Wavelet Transform (XWT) and Wavelet Coherence (WTC) approaches, which can detect asymmetric, nonlinear, and dynamic relationships in the short, medium, and long term, were used as the methodology. Empirical findings from WTC demonstrate the existence of a strong, co-directional movement between agriculture and GDP in the medium and long term, despite periodic differences. A high level of positive correlation was found between agriculture and CO2 emissions, particularly in long-term periods and after 1980. The direction of the phase arrows indicates that agricultural expansion triggers environmental degradation as a precursor. WTC results obtained from robustness analysis using the ecological footprint strongly confirm the long-term positive correlation between agriculture and environmental degradation. Consequently, this study empirically demonstrates that agricultural growth has a significant environmental cost. It is critically important for policymakers to implement green agriculture and low-carbon production strategies that prioritize environmental sustainability over purely economic growth and food security objectives in order to achieve environmentally friendly agricultural strategies.