The study, published in Discover Agriculture, examines the structural drivers of agricultural greenhouse gas emissions in Turkey from 1990 to 2021, a period spanning the country’s financial liberalization, deepening globalization, rapid technological investment, and expanding renewable energy capacity. Rather than treating agriculture in isolation, the authors model emissions as the outcome of an interconnected transformation process involving economic growth, financial development, renewable energy consumption, technological innovation, and globalization. Their central finding is striking: the relationship between income and agricultural emissions follows a U-shaped curve in the long term, not the classic inverted U-shape predicted by the Environmental Kuznets Curve hypothesis.

The Environmental Kuznets Curve has long underpinned policy optimism, holding that environmental degradation rises during early development but falls once societies grow wealthy enough to demand cleaner technologies and stronger regulation. The Turkish data tell a different story for agriculture. Below a turning point estimated at roughly 5,430 US dollars of per capita income in constant 2015 dollars, growth was associated with falling agricultural emissions, likely reflecting productivity gains and structural adjustment. But once that threshold was crossed, a milestone Turkey surpassed in the mid-1990s, further economic expansion became coupled with rising emissions, driven by intensifying food demand, mechanization, irrigation, livestock expansion, and heavier use of energy- and chemical-intensive inputs.

Methodologically, the study is notable for how it handles Turkey’s turbulent recent history. The 1994 currency crisis, the financial collapse of 2000 and 2001, and successive structural reforms all left marks on the country’s economic time series, and conventional unit root tests that ignore such breaks can produce misleading results. The authors therefore employed the fractional frequency Fourier Augmented Dickey-Fuller test, which uses sine and cosine functions to capture both sharp and gradual structural changes, alongside traditional ADF and Phillips-Perron tests. For the long-run estimates, they applied a Fourier-augmented autoregressive distributed lag approach, cross-checked with fully modified and canonical cointegrating regression estimators.

The Fourier components proved statistically significant, with an estimated fractional frequency of 2.42 indicating that the structural breaks embedded in Turkey’s development path are permanent rather than temporary. The error correction coefficient of -0.92 showed that short-term deviations from the long-run equilibrium are corrected rapidly, with roughly 92 percent of any imbalance eliminated within a single period. Diagnostic tests confirmed the absence of autocorrelation, heteroskedasticity, and model misspecification, and cumulative sum stability checks confirmed that the estimated coefficients remained stable across the sample.

Perhaps the most counterintuitive results concern renewable energy and technology. Both variables showed positive long-term relationships with agricultural emissions, meaning that the expansion of clean energy in the wider Turkish economy and the growth of patent activity have not translated into lower emissions from farming. The authors caution against reading these coefficients as direct causal effects. The renewable energy indicator measures the share of renewables in total final energy consumption economy-wide, not within agriculture, and total patent applications do not distinguish green or agricultural innovations from inventions in other sectors. The likely explanation is indirect: greater energy availability can stimulate irrigation, mechanization, processing, and land-use changes that expand production and offset emission savings.

Financial development told a similar story. According to the robustness estimates, a one percent increase in the financial development index was associated with approximately a 0.17 percent rise in agricultural emissions, suggesting that easier access to credit and a wider array of financial instruments fuel production expansion rather than environmental improvement. The finding aligns with a growing body of international evidence showing that finance, when not steered by environmental criteria, tends to amplify emission-intensive activity. The authors argue that this makes a case for green credit mechanisms and environmental quality conditions attached to agricultural lending.

Globalization emerged as the lone bright spot. In both the short and long term, increases in the KOF Globalization Index were associated with lower agricultural emissions, with the long-run coefficient implying that a one percent rise in globalization corresponds to roughly a one percent decline. The authors attribute this to the disciplining force of international markets: Turkey exports approximately half of its goods to European Union countries, whose emission standards and sustainability requirements push producers toward cleaner practices. Compliance with international environmental norms, they suggest, acts as a balancing factor that domestic growth dynamics alone fail to provide.

The broader implications reach well beyond Turkey. Roughly one-third of global greenhouse gas emissions originate from agriculture and food systems, and with the world population projected to reach 9.7 billion by 2050, pressure on farmland, water, and inputs will only intensify. The Turkish results suggest that for developing economies, growth alone will not deliver a low-carbon agricultural transition, and that finance, energy, and technology policies must be explicitly aligned with emission reduction targets. The authors recommend carbon pricing in agriculture, incentives for low-emission production, precision agriculture support, green patent programs, and renewable energy projects that deliver measurable on-farm reductions, such as energy-efficient irrigation and low-carbon machinery.

The study’s authors are candid about its limitations. The model does not directly include biophysical determinants such as fertilizer application, livestock density, land-use change, or climate variability, so the estimated relationships should be read as long-run associations rather than definitive causal effects. The patent and renewable energy indicators capture economy-wide activity rather than sector-specific innovation. Future research, they suggest, could extend the sample back to 1980, incorporate regional and farm-level data, apply asymmetric estimators, or compare Turkey with structurally similar economies in the European Union. But the core message stands: without deliberate sustainability policies, the engines of modern development may reproduce, rather than resolve, agriculture’s climate problem.

Subject of Research: Structural economic drivers of agricultural greenhouse gas emissions in Turkey

Article Title: Rethinking agricultural sustainability in Turkey: structural drivers of agricultural greenhouse gas emissions

Article References: Özbek, S., & Özbek, B. (2026). Rethinking agricultural sustainability in Turkey: structural drivers of agricultural greenhouse gas emissions. Discover Agriculture, 4(1), Article 302. https://doi.org/10.1007/s44279-026-00776-0

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00776-0

Keywords: agricultural emissions, greenhouse gases, Environmental Kuznets Curve, Turkey, financial development, renewable energy, technological innovation, globalization, econometrics, sustainable agriculture, climate change, low-carbon transition

The research, carried out by Baptiste Moreau, Alfonso Caiazzo, Thomas Amon, Long Chen and David Janke, set out to evaluate an open-source lattice Boltzmann solver called OpenLB against the established finite volume solver OpenFOAM. Conventional computational fluid dynamics tools used in agriculture solve the Navier–Stokes equations with finite volume or finite element methods, approaches that have been extensively validated over decades and are embedded in mature commercial and open-source packages. Their weakness is computational cost: as turbulence develops and domains grow, simulations can run for hours or days, which makes them poorly suited to applications where predictions are needed quickly, such as interpreting data streaming from barn-integrated sensor networks.

The lattice Boltzmann method takes a fundamentally different route to the same physics. Instead of discretizing the Navier–Stokes equations directly, it evolves a pseudo-compressible formulation on a fixed Cartesian lattice, tracking population densities that represent particles moving along a discrete set of velocities, commonly nineteen directions in three dimensions, the so-called D3Q19 model. Each time step consists of a collision step, in which the densities relax toward an equilibrium distribution described by the Bhatnagar–Gross–Krook operator, and a propagation step, in which information moves to neighboring nodes. The crucial property is locality: the collision step, which carries most of the computational burden, depends only on values at a single lattice node. Combined with a structured grid and explicit time-stepping, this makes the method exceptionally well suited to massive parallelization on modern hardware, including graphics processing units.

To test whether this promise holds for agricultural flows, the team built a benchmark that others can easily reproduce. They used a 1:100 scaled model of a typical agricultural building, 0.25 meters wide and 0.13 meters high, mounted perpendicular to the flow in the atmospheric boundary layer wind tunnel at the ATB Potsdam. The model, originally part of a dairy farm study, was elevated on a smooth table to minimize the influence of the tunnel’s thick boundary layer, producing an inflow with a nearly constant vertical velocity and turbulence profile. With the barn height as the characteristic length and an inlet velocity of 13 meters per second, the flow reached a Reynolds number of roughly 114,000, firmly in the turbulent regime. Velocity measurements were collected along fourteen vertical sample lines upstream and downstream of the model.

On the computational side, the barn geometry was supplied as an STL file and embedded in a domain about three meters long, 0.98 meters high and two meters wide. OpenLB generates a uniform lattice whose spacing is set by a refinement parameter, which the researchers varied from coarse grids of about 115,000 cells to finely resolved lattices exceeding 50 million cells. No-slip conditions on the ground and barn surfaces were enforced with a second-order interpolation scheme due to Bouzidi, a uniform inlet velocity was ramped up smoothly to suppress nonphysical oscillations, and slip conditions closed the top and sides. Turbulence was handled with the Smagorinsky large-eddy subgrid model, implemented in the lattice Boltzmann framework by adding an eddy viscosity to the relaxation time, with a Smagorinsky constant of 0.15 chosen after a preliminary sensitivity study.

The accuracy assessment compared time-averaged velocity components against the wind tunnel data using a battery of error metrics, including mean relative error, mean absolute error, root mean square error and a symmetric mean absolute percentage error. For the streamwise velocity, the simulation reproduced the qualitative trends of the experimental profiles along essentially all sampling lines, and about half of all measurement points showed relative errors below 13 percent. In regions dominated by the interaction with the building, such as lines immediately beside and behind the barn, agreement was partly very good, with errors below 5 percent. Larger deviations appeared for the vertical velocity component just upstream of the model, where the solver underestimated the measurements, possibly because a single constant Smagorinsky coefficient overestimates turbulent viscosity in the nearly turbulence-free inflow. The authors note that dynamic subgrid models and local grid refinement could improve these regions, and they emphasize that the achieved accuracy is consistent with previous validation studies of open-source solvers against comparable wind tunnel data.

The efficiency results are where the study becomes striking. Measured in million lattice updates per second, the CPU-based parallelization sustained roughly 100 MLUPs on refined meshes with 20 cores, at the upper end of values reported in the literature, and showed strong scaling with per-processor throughput declining only from 6.2 to 5.0 as core counts grew. Against OpenFOAM on the same grid, the lattice Boltzmann solver was about 23 times faster when both used 40 CPU cores, delivering a time ratio of 27 compared with 620 for the finite volume solver. The finite volume code’s scalability also broke down beyond roughly 35 cores, whereas OpenLB continued to benefit from additional processors. Moving to the server’s NVIDIA A100 GPUs brought a further speedup of about a factor of ten over MPI, reaching nearly 2,500 MLUPs on the finest meshes, a performance in line with the best lattice Boltzmann implementations reported elsewhere.

Combining these effects, the validated benchmark case achieved an overall acceleration of approximately 46-fold relative to the OpenFOAM reference, while for the most refined case examined, about 22 million cells, the combined CPU and GPU gains suggest a potential speedup approaching 230-fold, a figure the authors present as an indication of scalability rather than a validated result. True real-time simulation, where computation keeps pace with simulated time, was not achieved: GPU runs showed time ratios between 10 and 100. Even so, the researchers point out that multiple GPUs could close much of the remaining gap, and that the approach is already accessible far beyond supercomputing centers, since graphics cards with around 6,000 CUDA cores suitable for such computations are available for roughly 600 euros, raising the prospect of transient airflow simulations on a standard laptop.

The authors are careful to delineate the limits of their findings. The benchmark used a closed building, a nearly uniform inflow profile and an incompressible turbulent flow on a structured Cartesian lattice, precisely the class of problems for which the lattice Boltzmann method is best suited. More realistic scenarios involving internal ventilation, complex geometries, turbulent approaching flows or strongly coupled transport processes may show smaller efficiency gains and require case-by-case evaluation. A key constraint of the current solver version is its multi-block approach, which does not yet support local grid refinement, forcing uniform fine resolution across the entire domain and straining GPU memory in multiscale problems; a forthcoming release with local refinement is expected to be assessed in future work. Nevertheless, the study concludes that the open-source framework delivers accuracy comparable to established finite volume solvers at a fraction of the computational cost, positioning lattice Boltzmann methods as a credible foundation for the fast, transient dispersion simulations needed to quantify and mitigate emissions from naturally ventilated livestock buildings.

Subject of Research: Performance assessment of an open-source lattice Boltzmann framework for accelerated turbulent airflow simulation around agricultural buildings

Article Title: Accelerating airflow simulations for agricultural applications: Performance assessment of an open-source lattice Boltzmann framework

Article References: Moreau, B., Caiazzo, A., Amon, T., Chen, L., & Janke, D. (2026). Accelerating airflow simulations for agricultural applications: Performance assessment of an open-source lattice Boltzmann framework. Results in Engineering, 32, Article 112822. https://doi.org/10.1016/j.rineng.2026.112822

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.112822

Keywords: lattice Boltzmann method, OpenLB, computational fluid dynamics, livestock housing, natural ventilation, GPU computing, OpenFOAM, turbulence modeling, wind tunnel validation, agricultural emissions, high performance computing, near-real-time simulation

The idea of a food emissions tax is deceptively simple. Just as carbon pricing has been applied to fossil fuels, a food tax would charge producers or consumers in proportion to the climate pollution associated with growing, raising, and transporting what ends up on the plate. Beef and lamb, which carry the heaviest emissions footprints per kilogram because of methane from ruminants and the land required for pasture and feed, would face the steepest charges. Plant-based staples such as grains, legumes, and most fruits and vegetables, with far smaller footprints, would see little or no penalty. The economic logic is that when the price of high-emission foods rises, consumers substitute toward cheaper, cleaner alternatives, and producers shift resources accordingly.

The central worry, however, has always been leakage. If one country or region taxes emissions-intensive foods while its neighbours do not, production of those foods may simply migrate, moving to untaxed jurisdictions where the climate advantage of the policy is eroded. Worse, some feared, a tax could push consumption patterns in perverse directions, with consumers swapping taxed beef for untaxed but equally polluting alternatives, or with domestic production cuts being offset by imports raised under laxer conditions. Critics have pointed to the history of unilateral climate policies, where carbon-intensive industries sometimes relocated abroad, leaving global emissions unchanged or even higher, to argue that food taxes would be similarly undermined.

The new study puts those fears to a rigorous quantitative test. Using an economic modelling framework that couples global agricultural production with international trade and consumer demand, the researchers simulated emissions-based food taxes across multiple scenarios, ranging from unilateral action by a single large economy to coordinated pricing implemented by major emitters simultaneously. In each scenario, they tracked not only emissions from agriculture and land use but also changes in what people actually eat, and from those dietary shifts they estimated consequences for chronic disease and premature mortality.

The results are striking on both fronts. On the health side, the tax produces meaningful reductions in consumption of red and processed meats in the regions where it applies, and because red and processed meat are associated with elevated risks of cardiovascular disease, type 2 diabetes, and colorectal cancer, the dietary shift translates into fewer premature deaths. The health benefits are not confined to wealthy countries. Because food prices ripple through international markets, the analysis finds dietary improvements and associated mortality reductions spreading well beyond the taxing regions, as trade adjustments alter the relative prices of meats and plant proteins on global markets.

On the climate side, the tax directly reduces agricultural emissions by shrinking production of the most polluting commodities, and it produces additional gains by easing pressure on land. When demand for ruminant meat falls, less pasture and feed cropland is needed, creating space for forests and other carbon-rich ecosystems to recover or remain intact. Land-use change is one of the largest sources of food-system emissions, so even modest reductions in grazing and feed demand can compound into significant carbon savings. The modelling shows these effects are robust across scenarios, though naturally largest where the tax covers the most countries and the most emissions-intensive products.

Perhaps the most consequential finding concerns leakage itself. Across the scenarios examined, the analysis finds that emissions leakage from food taxes is limited rather than pervasive. Some production does shift to untaxed regions, but the scale of that shift is small relative to the overall emission reductions achieved, so the net global effect remains strongly positive. The study attributes this to several structural features of agricultural markets. Food production is tied to land, climate, and infrastructure in ways that industrial manufacturing is not, making relocation slower and costlier. Consumer tastes are also sticky, so demand in taxing regions falls rather than simply redirecting to imports. And because the health benefits arise from reduced consumption of harmful foods, they accrue regardless of exactly where the avoided production would otherwise have taken place.

Still, the study is careful to identify where policy design matters. Leakage is lowest when major producing regions act in concert, which argues for coordinated or trade-linked approaches rather than isolated unilateral taxes. The design of the tax base is also critical: pricing that covers emissions from land-use change, in addition to direct production emissions, captures more of the true climate cost of food and reduces the incentive to shift production toward commodities whose footprints are understated when deforestation is ignored. The analysis also points to the distributional question that any food tax must confront, since food spending consumes a larger share of income for poorer households. The modelling suggests that the health benefits themselves are progressive, falling disproportionately on lower-income groups that suffer the highest burdens of diet-related disease, but the authors note that pairing a tax with rebates or targeted support could cushion any regressive price effects.

The findings arrive at a moment when the food system accounts for roughly a third of global greenhouse gas emissions and shows few signs of decarbonizing on its own. Agricultural emissions are notoriously difficult to address through technology alone: methane from cattle lacks an inexpensive abatement fix at scale, and demand growth for meat in emerging economies continues to outpace efficiency gains. Command-and-control approaches to diet are politically fraught, and voluntary dietary guidance has moved consumption little. Pricing instruments occupy the middle ground, working through the same market mechanisms that have driven demand growth, but in the opposite direction. The evidence that such instruments can operate with limited leakage strengthens the case for incorporating food into carbon pricing frameworks that to date have focused almost entirely on energy and industry.

The research also adds a dimension that pure climate accounting has missed. Because the same foods that drive emissions also drive disease, a single instrument simultaneously addresses two of the largest policy challenges of the century, and the health co-benefits alone may be large enough to justify the policy even before climate damages are counted. For governments weighing the political cost of taxing food, that combination matters: it widens the coalition of beneficiaries, from climate ministries to health systems straining under the cost of diet-related illness, and it offers a narrative in which the policy is not merely a burden but an investment whose returns arrive in fewer hospitalizations and longer lives. As negotiations over how to bring agriculture into climate policy continue, this analysis suggests that the leakage problem, long invoked as a reason to delay, is a manageable design challenge rather than a fatal flaw, and that well-crafted food emissions taxes can be a powerful, double-duty instrument for both planetary and human health.

Subject of Research: Modeling the health and climate effects of food emissions taxes, including production leakage, across global agricultural trade scenarios.

Article Title: Food emissions taxes can deliver health and environmental benefits with limited leakage

Article References: Bouyssou, C. G., Springmann, M., Clora, F., Jensen, J. D., & Yu, W. (2026). Food emissions taxes can deliver health and environmental benefits with limited leakage. Nature Food. https://doi.org/10.1038/s43016-026-01429-7

Image Credits: AI Generated

DOI: 10.1038/s43016-026-01429-7

Keywords: food emissions tax, carbon pricing, leakage, global health, red meat, dietary change, agricultural emissions, land use, climate policy, trade modelling, ruminant methane, mortality

The research, published in Cleaner Engineering and Technology, arrives at a moment when agriculture’s climate accounting is under intensifying scrutiny. Farming contributes an estimated 10 to 15 percent of global greenhouse gas emissions, and China, the world’s largest carbon emitter, has pledged to peak its emissions by 2030 and achieve carbon neutrality by 2060. Yet most carbon footprint studies of fruit production have examined only isolated stages, such as planting or transport, leaving the cumulative impact of processing and packaging largely unmeasured. The team behind the new work argues that this fragmentation has prevented the emergence of genuinely comprehensive low-carbon strategies, and they set out to close the gap by treating the whole industrial chain, from orchard to processed product, as a single accounting and optimization problem.

Qiongzhong County offered an ideal case. Its tropical monsoon climate, with an average annual temperature of 22.8 degrees Celsius and rainfall of 2,444 millimeters, suits citrus cultivation well, and the Qiongzhong-Lvcheng orange carries significant economic weight in the region. Because the local industry is still young, it currently relies on just a handful of product pathways: bulk fruit sold as harvested, standardized graded fruit, and small volumes of green orange wine and green orange vinegar brewed from secondary fruit that cannot be sold whole. The researchers extended this real-world chain hypothetically to include concentrated juice and not-from-concentrate juice, drawing processing data from comparable citrus operations in the literature, and defined six functional units, each based on one tonne of harvested oranges flowing to a different final product.

The carbon accounting revealed stark differences among the pathways. Planting one tonne of oranges for bulk sale generates 165.1 kilograms of CO2-equivalent, while graded fruit comes in at 180.1 kilograms. Diverting the same tonne to concentrated juice raises the footprint to 282.1 kilograms, to NFC juice 188.7 kilograms, to green orange wine 385.1 kilograms, and to green orange vinegar a striking 796.8 kilograms. The economics tell a different story. Graded fruit earns a profit of about 18,060 yuan per tonne of input, green orange wine roughly 20,270 yuan, and green orange vinegar the highest at about 22,790 yuan. Concentrated juice is the outlier in every sense: it carries a high carbon footprint and a negative profit of roughly 2,660 yuan per tonne of oranges, because the energy-hungry concentration and refrigeration stages consume far more value than the tiny 0.063-tonne juice yield recovers.

Dissecting the emissions further, the study found that fertilizers and pesticides dominate the planting stage, contributing 53 percent and 43 percent of its footprint respectively. Organic fertilizer alone accounts for 48 percent, not because it is carbon-intensive per kilogram, but because nearly two tonnes of it are applied per tonne of fruit. Insecticides, with an emission factor of 16.61 kilograms of CO2-equivalent per kilogram, contribute 38 percent, a burden driven by the orange’s acute vulnerability to Huanglong disease and red spider mites, which force heavy pesticide applications. For the processed products, packaging emerges as a surprisingly powerful lever: glass bottles account for 60.8 percent of green orange vinegar’s total footprint and 18.9 percent of green orange wine’s, while concentration, cooling, and refrigeration dominate the concentrated juice pathway.

With these per-product profiles established, the team turned to optimization. They employed NSGA-II, a widely used evolutionary algorithm, to search for the allocation of harvested oranges across the six products that best balances three competing objectives: maximizing economic profit, minimizing carbon footprint, and limiting industrial instability, measured through the coefficient of variation of the product mix. Two constraints grounded the simulation in reality. At least 20 percent of the harvest must flow to deep processing, reflecting the share of secondary fruit that cannot be sold whole, and no product’s share may deviate from the current structure by more than 30 percent, acknowledging that consumer demand and processing capacity cannot shift overnight. The algorithm, run with a population of 300 over 400 generations, produced a Pareto front of non-dominated solutions from which four representative scenarios were distilled.

The scenario results are where the study’s headline finding emerges. Under the economic development scenario, profit rises by 40.7 percent as oranges shift toward graded fruit, green orange wine, and green orange vinegar, but total emissions climb 63.7 percent. The low-carbon development scenario, which channels roughly two-thirds of the harvest into bulk and graded fruit and the remainder into green orange wine and NFC juice, raises profit by 10.0 percent while holding the carbon increase to just 7.9 percent. The stabilized development scenario spreads the harvest almost evenly across all products, cutting the industrial instability index from 1.42 to 0.10, though it sacrifices 5.1 percent of profit and raises emissions by 63.9 percent. The comprehensive development scenario, concentrating on the three best-performing products, lifts profit 31.8 percent with a 19.4 percent carbon increase, but leaves instability relatively high at 1.18.

For the researchers, the low-carbon pathway stands out as the most immediately actionable. It demonstrates that a modest, targeted expansion of deep processing, one that fully utilizes the roughly 20 percent of fruit currently sold off cheaply as secondary produce, can simultaneously raise incomes, diversify the product portfolio, and keep emissions nearly flat. The current industry, by contrast, is highly exposed: with 49.5 percent of oranges sold in bulk and 44.5 percent as graded fruit, and only 6 percent processed, the sector’s total profit of 128 million yuan rests on a fragile, single-structure foundation that leaves it vulnerable to fresh-fruit market swings. Sensitivity and Monte Carlo analyses, involving 10,000 simulated iterations, confirmed that the ranking of products by carbon footprint remains robust even under wide uncertainty in the input data.

The implications extend well beyond one county’s citrus groves. The planting stage accounts for more than 85 percent of the footprint of primary products, a pattern consistent with citrus studies in Spain and apple studies in China, and the dominance of fertilizers and pesticides echoes findings in other intensively managed crops. The team’s recommendations, improving organic fertilizer efficiency, strengthening physical pest control, recycling CO2 from fermentation and distillation, shifting processing energy to renewables, and adopting lighter, recyclable packaging, are transferable to most fruit and vegetable value chains. For policymakers in Qiongzhong, the study points toward subsidies that make low-carbon production visibly valuable, standardized cultivation practices, and a deliberately diversified processing sector. For the broader challenge of reconciling agricultural livelihoods with climate targets, it offers a concrete demonstration that the trade-off between profit and emissions, while real, is far more manageable than a single-minded focus on either goal would suggest.

Subject of Research: Whole-chain carbon footprint accounting and multi-objective optimization of the Qiongzhong-Lvcheng orange industrial chain in Hainan, China

Article Title: Value-chain optimization enhances economic resilience and profits with moderate carbon footprint increase: Qiongzhong-Lvcheng orange case

Article References: Gong, C., Yu, Q., He, K., Dong, X., Guo, Q., Cai, Y., He, L., Varbanov, P. S., & Wang, X.-C. (2026). Value-chain optimization enhances economic resilience and profits with moderate carbon footprint increase: Qiongzhong-Lvcheng orange case. Cleaner Engineering and Technology, 34, Article 101318. https://doi.org/10.1016/j.clet.2026.101318

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101318

Keywords: carbon footprint, life cycle assessment, value chain optimization, citrus production, NSGA-II, agricultural emissions, economic resilience, green orange wine, Qiongzhong-Lvcheng orange, sustainable agriculture, multi-objective optimization, Hainan