Search Results (101)

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Understanding the spatio-temporal evolution of farmland carbon emissions, disentangling their underlying driving forces, and exploring the decoupling relationship between these emissions and economic development are pivotal to advancing low-carbon and high-quality agricultural development in Shandong Province, China. Using the Logarithmic Mean Divisia Index (LMDI) and Tapio decoupling model, this study conducted a comprehensive analysis of panel data from 16 cities in Shandong Province spanning 2004–2023. This research reveals that the total farmland carbon emissions in Shandong Province followed a trajectory of “initial fluctuating increase and subsequent steady decline” during the study period. The emissions peaked at 29.4 million tons in 2007 and then declined to 20.2 million tons in 2023, representing a 26.0% reduction compared to the 2004 level. Farmland carbon emission intensity in Shandong Province showed an overall downward trend over the period 2004–2023, with the 2023 intensity registering a 68.9% decline compared to 2004. The carbon emission intensity, agricultural structure, and labor effects acted as inhibiting factors on farmland carbon emissions in Shandong Province, while the economic development effect exerted a positive driving impact on the growth of such emissions. Over the 20-year period, these four factors cumulatively contributed to a reduction of 2.1 × 10⁵ tons in farmland carbon emissions. During 2004–2013, the farmland carbon emissions in Zaozhuang, Yantai, Jining, Linyi, Dezhou, Liaocheng, and Heze showed a weak decoupling state, while in 2014–2023, the farmland carbon emissions and economic development in all cities of Shandong Province showed a strong decoupling state. In the future, it is feasible to reduce farmland carbon emissions in Shandong Province by improving agricultural resource utilization efficiency through technological progress, adopting advanced low-carbon technologies, and promoting the transformation of agricultural industrial structures towards “high-value and low-carbon” designs. Full article

The salinization of agricultural soils is a serious threat to farming and ecological balance in arid and semi-arid regions. Accurate estimation of soil water-soluble ions (calcium, carbonate, magnesium, and sulfate) is necessary for correct monitoring of soil salinization and sustainable land management. Hyperspectral ground-based data are valuable in soil salinization monitoring, but the acquisition cost is high, and the coverage is small. Therefore, this study proposes a two-stage deep learning framework with multispectral remote-sensing images. First, the wavelet transform is used to enhance the Transformer and extract fine-grained spectral features to reconstruct the ground-based hyperspectral data. A comparison of ground-based hyperspectral data shows that the reconstructed spectra match the measured data in the 450–998 nm range, with R² up to 0.98 and MSE = 0.31. This high similarity compensates for the low spectral resolution and weak feature expression of multispectral remote-sensing data. Subsequently, this enhanced spectral information was integrated and fed into a novel multiscale self-attentive Transformer model (MSATransformer) to invert four water-soluble ions. Compared with BPANN, MLP, and the standard Transformer model, our model remains robust across different spectra, achieving an R² of up to 0.95 and reducing the average relative error by more than 30%. Among them, for the strongly responsive ions magnesium and sulfate, R² reaches 0.92 and 0.95 (with RMSE of 0.13 and 0.29 g/kg, respectively). For the weakly responsive ions calcium and carbonate, R² stays above 0.80 (RMSE is below 0.40 g/kg). The MSATransformer framework provides a low-cost and high-accuracy solution to monitor soil salinization at large scales and supports precision farmland management. Full article

This study investigates the impact of rural digital economy development on agricultural carbon emission efficiency, aiming to elucidate the intrinsic mechanisms and pathways through which digital technology enables low-carbon transformation in agriculture, thereby contributing to the achievement of agricultural carbon neutrality goals. Based on provincial-level panel data from China spanning 2011 to 2022, this study examines the relationship between the rural digital economy and agricultural carbon emission efficiency, along with its underlying mechanisms, using bidirectional fixed effects models, mediation effect analysis, and Spatial Durbin Models. The results indicate the following: (1) A significant N-shaped-curve relationship exists between rural digital economy development and agricultural carbon emission efficiency. Specifically, agricultural carbon emission efficiency exhibits a three-phase trajectory of “increase, decrease, and renewed increase” as the rural digital economy advances, ultimately driving a sustained improvement in efficiency. (2) Industrial integration acts as a critical mediating mechanism. Rural digital economy development accelerates the formation of the N-shaped curve by promoting the integration between agriculture and other sectors. (3) Spatial spillover effects significantly influence agricultural carbon emission efficiency. Due to geographical proximity, regional diffusion, learning, and demonstration effects, local agricultural carbon emission efficiency fluctuates with changes in neighboring regions’ digital economy development levels. (4) The relationship between rural digital economy development and agricultural carbon emission efficiency exhibits a significant inverted N-shaped pattern in regions with higher marketization levels, planting-dominated areas of southeast China, and digital economy demonstration zones. Further analysis reveals that within rural digital economy development, production digitalization and circulation digitalization demonstrate a more pronounced inverted N-shaped relationship with agricultural carbon emission efficiency. This study proposes strategic recommendations to maximize the positive impact of the rural digital economy on agricultural carbon emission efficiency, unlock its spatially differentiated contribution potential, identify and leverage inflection points of the N-shaped relationship between digital economy development and emission efficiency, and implement tailored policy portfolios—ultimately facilitating agriculture’s green and low-carbon transition. Full article

The advantages of torrefaction preheating, including the production of a hydrophobic solid product, improved particle size distribution, enhanced fuel properties with fewer environmental issues, decreased moisture content, and reduced volatile content. In order to meet the technical requirements of biomass oriented value-added and energy saving and emission reduction, pine sawdust (PS) was taken as the research object, and the physicochemical properties of the PS samples preheated at a low temperature were analyzed by synchronous thermal analysis (TG-DSC), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and organic element analyzer (EA). The effect of preheating at a lower temperature on the physicochemical properties of PS was discussed. The results showed that, under the preheating condition of 200 °C, compared with PS, the water content of PS-200 decreased by 3.23%, the volatile content decreased by 3.69%, the fixed carbon increased by 6.81%, the calorific value increased by 6.90%, the equilibrium water content decreases from 7.06% to 4.46%, and the hydrophobicity increases. This research, based on the improvement of the quality of agricultural and forestry waste and the promotion of the strategy of converting waste into energy, has contributed to the advancement of sustainable energy production. Full article

To achieve regional sustainable development, the low-carbon transformation of agriculture is essential, as it serves both as a significant carbon source and as a potential carbon sink. This study calculated the agricultural carbon emissions in Liaocheng from 2010 to 2022 by analyzing processes including crop cultivation, animal husbandry, and agricultural input. Additionally, a simulation model of the water–energy–food–carbon nexus (WEFC-Nexus) for Liaocheng’s agricultural production process was developed. Using Vensim PLE 10.0.0 software, this study constructed a WEFC-Nexus model encompassing four major subsystems: economic development, agricultural production, agricultural inputs, and water use. The model explored four policy scenarios: business-as-usual scenario (S1), ideal agricultural development (S2), strengthening agricultural investment (S3), and reducing agricultural input costs (S4). It also forecast the trends in carbon emissions and primary sector GDP under these different scenarios from 2023 to 2030. The conclusions were as follows: (1) Total agricultural carbon emissions exhibited a three-phase trajectory, namely, “rapid growth (2010–2014)–sharp decline (2015–2020)–gradual rebound (2021–2022)”, with sectoral contributions ranked as livestock farming (50%) > agricultural inputs (27%) > crop cultivation (23%). (2) The carbon emissions per unit of primary sector GDP (CEAG) for S2, S3, and S4 decreased by 8.86%, 5.79%, and 7.72%, respectively, compared to S1. The relationship between the carbon emissions under the four scenarios is S3 > S1 > S2 > S4. The relationship between the four scenarios in the primary sector GDP is S3 > S2 > S4 > S1. S2 can both control carbon emissions and achieve growth in primary industry output. Policy recommendations emphasize reducing chemical fertilizer use, optimizing livestock management, enhancing agricultural technology efficiency, and adjusting agricultural structures to balance economic development with environmental sustainability. Full article

Lignin, the most abundant renewable aromatic biopolymer on Earth, is rapidly emerging as a powerful enabler of next-generation sustainable technologies. This review shifts the focus to the latest industrial breakthroughs that exploit lignin’s multifunctional properties across energy, agriculture, healthcare, and environmental sectors. Lignin-derived carbon materials are offering scalable, low-cost alternatives to critical raw materials in batteries and supercapacitors. In agriculture, lignin-based biostimulants and controlled-release fertilizers support resilient, low-impact food systems. Cosmetic and pharmaceutical industries are leveraging lignin’s antioxidant, UV-protective, and antimicrobial properties to create bio-based, clean-label products. In water purification, lignin-based adsorbents are enabling efficient and biodegradable solutions for persistent pollutants. These technological leaps are not merely incremental, they represent a paradigm shift toward a materials economy powered by renewable carbon. Backed by global sustainability roadmaps like the European Green Deal and China’s 14th Five-Year Plan, lignin is moving from industrial residue to strategic asset, driven by unprecedented investment and cross-sector collaboration. Breakthroughs in lignin upgrading, smart formulation, and application-driven design are dismantling long-standing barriers to scale, performance, and standardization. As showcased in this review, lignin is no longer just a promising biopolymer, it is a catalytic force accelerating the global transition toward circularity, climate resilience, and green industrial transformation. The future of sustainable innovation is lignin-enabled. Full article

Addressing the sustainability challenges posed by the expanding livestock sector is crucial for China’s green transition. With the transformation of national dietary structure and increasing demand for livestock products, the associated resource consumption and environmental impacts, particularly carbon emissions have intensified. Reducing carbon emissions from livestock is vital for mitigating global warming, enhancing resource utilization efficiency, improving ecosystems and biodiversity, and ultimately achieving sustainable development of the livestock industry. Against this backdrop, this study measures the carbon emissions from livestock sector employing the Life Cycle Assessment (LCA) method, and applies the Generalized Divisia Index Method (GDIM) to analyze the factors affecting the changes in carbon emissions, aiming to quantify and analyze the carbon footprint of China’s livestock sector to inform sustainable practices. The findings reveal that China’s total carbon emissions from the livestock sector fluctuated between 645.15 million tons and 812.99 million tons from 2000 to 2023. Since 2020, emissions have entered a new phase of continuous growth, with a 5.40% increase in 2023 compared to 2020. Significantly, a positive trend toward sustainability is observed in the substantial decline of carbon emission intensity over the study period, with notable reductions in emission intensity across provinces and a gradual convergence in inter-provincial disparities. Understanding the drivers is key for effective mitigation. The output level and total mechanical power consumption level emerged as primary positive drivers of carbon emissions, while output carbon intensity and mechanical power consumption carbon intensity served as major negative drivers. Moving forward, to foster a sustainable and low-carbon livestock sector, China’s livestock sector development should prioritize coordinated carbon reduction across the entire industrial chain, adjust the industrial structure, and enhance the utilization efficiency of advanced low-carbon agricultural machinery while introducing such equipment. Full article

In this study, sugarcane bagasse (SCB), an abundant agricultural byproduct, was transformed into activated carbon via a controlled thermochemical pyrolysis route for high-performance energy storage applications. Herein, we utilized the activated carbon derived from pure sugarcane bagasse (SCB-AC) and further activated using KOH (SCB-KOH-AC) as an electrode material in aqueous symmetric supercapacitor configurations. The synthesized activated carbon was subjected to analysis using a range of characteristics including FT-Raman spectroscopy, which was employed to confirm the functional groups present in the carbon materials. The XPS analysis provided insights into the elemental composition and ionic states. The SEM analysis revealed that both activated carbon and KOH/activated carbon materials exhibited a layered or stacked, albeit slightly random, orientation. Electrochemical studies demonstrated that the synthesized carbon electrodes exhibited impressive specific capacitance values of (SCB) activated carbon (132.20 F/g) and KOH-activated, pure SCB AC (SCB-A) 253.41 F/g at 0.5 A/g. Furthermore, the SCB KOH-activated carbon (AC) electrode revealed a higher specific capacitance value and A//SCB-A symmetric devices delivered energy density reaching 17.91 Wh/kg and power density up to 2990 W/kg. The KOH-activated carbon electrode demonstrated remarkable cycling stability retaining 93.89%, even after 10,000 cycles. These results suggest that the sugarcane bagasse-derived activated carbon is a sustainable and low-cost candidate for next-generation supercapacitor electrodes. The results demonstrate enhanced capacitance, stability, and pore structure tailored for energy storage applications. The KOH-activated carbon SCB carbon symmetric device with symmetric electrodes exhibited a suitable bio-mass carbon for future energy storage applications. Full article

In the context of agricultural green transformation, the balance between the environmental footprint and economic return is a key indicator for measuring the synergy of high yields, high efficiency, and environmental friendliness in agricultural systems. However, the pathways and mechanisms for achieving this synergy in orchard systems remain unclear. Based on a three-year field experiment in Pinghe County, Fujian Province, a comprehensive evaluation framework integrating life cycle assessment (LCA) was constructed. This framework was used to systematically analyze the differences in the net ecosystem economic benefit (EEB) and environmental impact of four fertilization regimes: the conventional farming regime with no mulching (A; 1084 kg N ha⁻¹, 914 kg P₂O₅ ha⁻¹, and 906 kg K₂O ha⁻¹), the conventional farming regime with mulching (B), the optimized fertilization regime with water–fertilizer integration (C; 250 kg N ha⁻¹, 200 kg K₂O ha⁻¹, 100 kg MgO ha⁻¹, and 400 kg CaO ha⁻¹), and the optimized fertilization regime with controlled-release fertilizers (D). The results showed that regime D performed best in terms of yield, nutrient-use efficiency, and EEB, which increased by 220.5% and 297.5% compared with regime A, and reduced the input cost by CNY 63,100~69,000 hm⁻². Moreover, compared with regime A, regimes B, C, and D significantly reduced the carbon, nitrogen, and phosphorus footprints, respectively, with the carbon footprint reduced by 6.7~21.7%, 72.4~74.8%, and 71.6~76.5%; the nitrogen footprint reduced by 2.6~19.0%, 80.7~82.2%, and 80.1~83.4%; and the phosphorus footprint reduced by 15.3%, 100%, and 100%. Furthermore, the comprehensive evaluation index (CEI) is D > C > B > A. In total, the three optimized regimes balanced high yield with environmental sustainability, with the D regime showing the best performance, offering scientific support for transitioning to low-carbon, high-value orchards in smallholder systems. Full article

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) targets. To reveal the spatiotemporal evolution characteristics and complex driving mechanisms of agricultural carbon emissions (ACEs), this study constructs a comprehensive accounting framework for agricultural carbon emissions based on provincial panel data from China spanning 2000 to 2023. The framework encompasses three major carbon sources—cropland use, rice cultivation, and livestock farming—enabling precise quantification of total agricultural carbon emissions. Furthermore, spatial-temporal distribution patterns are characterized using methodologies including standard deviational ellipse (SDE) and spatial autocorrelation analysis. For driving mechanism identification, the Geodetector and Geographically and Temporally Weighted Regression (GTWR) models are employed. The former quantifies the spatial explanatory power and interaction effects of driving factors, while the latter enables dynamic estimation of factor influence intensities across temporal and spatial dimensions, jointly revealing significant spatiotemporal heterogeneity in driving mechanisms. Key findings: (1) temporally, total ACEs exhibit fluctuating growth, while emission intensity has significantly decreased, indicating the combined effects of policy regulation and technological advancements; (2) spatially, emissions display an “east-high, west-low” pattern, with an increasing number of hotspot areas and a continuous shift of the emission centroid toward the northwest; and (3) mechanistically, agricultural gross output value is the primary driving factor, with its influence fluctuating in response to economic and policy changes. The interactions among multiple factors evolve over time, transitioning from economy-driven to synergistic effects of technology and climate. The GTWR model further reveals the spatial and temporal variations in the impacts of each factor. This study recommends formulating differentiated low-carbon agricultural policies based on regional characteristics, optimizing industrial structures, enhancing modernization levels, strengthening regional collaborative governance, and promoting the synergistic development of climate and agriculture. These measures provide a scientific basis and policy reference for achieving the “dual carbon” goals. Full article

Regenerative agriculture has emerged as an innovative approach to food production, offering the potential to achieve reduced or even positive environmental and social outcomes compared to the soil degradation and greenhouse gas emissions of conventional agriculture. Simultaneously, a sophisticated dual-use system combining solar energy generation from photovoltaics with agricultural production, called agrivoltaics, is rapidly expanding. Combining these approaches into regenerative agrivoltaics offers a promising solution to the challenges regarding food in a rapidly warming world. This review theoretically examines the compatibility and mutual benefits of combining agrivoltaics and regenerative agriculture while also identifying the challenges, opportunities, and pathways for implementing this system. A foundation for advancing regenerative agrivoltaics is made by identifying areas for research, which include the following: (1) carbon sequestration, (2) soil health and fertility, (3) soil moisture, (4) soil microbial activity, (5) soil nutrients, (6) crop performance, (7) water-use efficiency, and (8) economics. By addressing the intersection of agriculture, renewable energy, and sustainability, regenerative agrivoltaics emphasizes the transformative potential of integrated systems in reshaping land use and resource management. This evaluation underscores the importance of policy and industry collaboration in facilitating the adoption of regenerative agrivoltaics, advocating for tailored support mechanisms to enable widespread implementation of low-cost, zero-carbon, resilient food systems. Full article

Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object; measures the level of ACE based on the panel data from 2008 to 2022; and analyzes the development and influencing factors of the coupling and coordination between ACE and AEG by using the coupling coordination degree model, the Dagum Gini coefficient decomposition method, and the Tobit regression model. The results reveal the following: (1) The overall ACE in Jiangxi Province displays a significant upward trend, with the average efficiency value increasing from 0.172 to 0.624, reflecting an average annual growth rate of 72.43%. Nonetheless, there remains clear regional heterogeneity, characterized by lower efficiencies in Central and Southern Jiangxi compared to the higher efficiencies found in Northern and Western Jiangxi. (2) Despite gradual improvements in regional coordination, the Central and Southern Jiangxi regions still lag Northern and Western Jiangxi in terms of the linked coordination between ACE and AEG, symptoms of which had been previously misaligned. (3) The results of Dagum’s Gini coefficient decomposition show that inter-regional disparities are the main source of overall disparities, with a contribution of 37.43%, which is higher than the synergistic effect of intra-regional disparities and hyper-variable densities, corroborating the core contradiction of uneven development across regions. (4) The Tobit model reveals that government investment, industrial structure optimization, urbanization, and educational attainment exert a significant positive influence on promoting coupling coordination. To establish a scientific basis for achieving a low-carbon agricultural transformation and equitable AEG in Jiangxi Province, this research recommends bolstering regional cooperation, fostering innovations in agricultural science and technology, optimizing the industrial structure, and enhancing farmers’ awareness of low-carbon practices. This study expands the theoretical system of agricultural low-carbon transition in terms of research methods and scales to provide a scientific basis for agricultural provinces to realize agricultural low-carbon transition and balanced economic development. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Agricultural science and technology policies (ASTPs) have played a pivotal role in shaping agricultural innovation, sustainability, and cleaner production practices. Understanding how ASTPs diffuse is essential for optimizing policy design and advancing the green transition in agriculture. This study aims to investigate the diffusion of ASTPs in China, using a quantitative citation-based approach. The goal is to explore diffusion patterns, topic characteristics, and historical trajectories of ASTPs, thereby providing insights into policy transmission mechanisms that can inform future policy improvements. We analyze 3207 ASTP documents, focusing on policy citation links to examine the distribution, diffusion characteristics, and dynamics of policies. The analysis includes tracking topic evolution and identifying key policies while estimating the main diffusion paths. The results show that the top-down diffusion model is the dominant pattern of policy transmission, exhibiting the highest diffusion speed and both short- and long-term impacts. ASTPs have progressively expanded toward industrialization, informatization, and green development, with increased policy transmission efficiency. The diffusion process has formed three primary pathways: (i) enhancing agricultural innovation capacity, (ii) accelerating the transformation of technological achievements, and (iii) improving the agricultural science and technology innovation system. These pathways are critical to advancing sustainable and cleaner agricultural production. This study provides valuable insights into the diffusion of ASTPs and highlights key pathways for policy optimization. The findings suggest that enhancing policy frameworks and improving policy implementation efficiency will be crucial for facilitating the transition toward sustainable, low-carbon, and environmentally friendly agricultural practices. Future research should refine data sources and incorporate semantic analysis to capture more detailed policy transmission mechanisms. Full article