Global Carbon Emissions Research Articles

Assessing the circular re-design of prefabricated building envelope elements for carbon neutral renovation

Buildings and the construction industry at large are significant contributors to the catastrophic climate breakdown. The built environment is responsible for 37% of the total global carbon emission, of which about a third arises from the energy used to produce building and construction materials, usually referred to as embodied carbon. One of the key strategies to reduce the environmental impact of buildings is to significantly improve their energy efficiency, which is referred to as deep renovation. Prefabricated building envelope elements intended to prevent heat loss through the building envelope are considered a key deep-renovation technology. Connecting prefabricated elements to a building reflects a potential stream of waste if applied linearly with severe negative environmental impact in terms of natural resource depletion and exposure to pollutants. This article reports on a quantitative Design for Disassembly (Dfd) indicator to assess future recovery potential and, subsequently, its impact on embodied carbon emission of the circular redesign of three different prefabricated building envelope elements. Although none of the redesigned elements are yet considered 100% circular, the development of these three prefabricated building envelope elements showcases that the environmental impact can be substantially reduced following a well-structured and dedicated innovation process. The reduction of the environmental impact is indicated by lower quantities of embodied carbon up to 50% and an improved design for disassembly, reflecting a higher reuse potential of building materials and components. Several limitations and directions for further research were identified to advance the development of circular, prefabricated deep-renovation building envelope elements.

Determining the Composition and Structure of a High-Performing SEI Layer for Lithium-Mediated Nitrogen Reduction to Ammonia

The industrial synthesis of ammonia (NH3) via the Haber-Bosch process has enabled mass production of fertilizer and the human population growth to 8 billion.1 NH3 also shows promise as a long-term, energy-dense fuel amenable to widespread distribution.2 However, the Haber-Bosch process operates at high temperatures and pressures in centralized facilities and accounts for 1.3% of global carbon emissions.1,2 To ensure a food-secure future and realize NH3 as a truly carbon-free fuel, we must develop an NH3 synthesis alternative that operates at ambient conditions and can be driven by renewables.One of the most promising NH3 synthesis method under mild conditions is the electrochemical, Li-mediated process (Li-NRR). In this process, Li is electrodeposited from solution and reacted with N2 to form Li3N. A proton shuttle, e.g., ethanol, protonates Li3N to form NH3.3 The electrodeposited Li forms a solid-electrolyte interphase (SEI) layer that controls N2, Li+, and H+ transport and is key to system performance. The use of fluorinated Li salts, e.g., LiBF4, have recently advanced Li-NRR system performance. Improved selectivity has been attributed to changes in the SEI layer, but evaluating SEI structure and composition remains a significant challenge.3 Neutron-based characterization methods, complementary to X-ray methods, offer unique benefits to study SEI layers containing light elements such as Li- and H; for example, an element’s neutron scattering cross section does not depend on its atomic number.4 In this work, we studied the formation of the SEI layer in-situ before and after applying -0.3 mA/cm2 in galvanostatic experiments (chronopotentiometry, CP) on a copper (Cu) working electrode. We utilized a combination of deuterated and non-deuterated solvents and proton donors to distinguish their contributions to the SEI. We found that the SEI forms two layers: a compact, lithium rich layer, and a diffuse layer with carbonaceous species (Figure 1).4 We also identified the presence of boron in the layers, indicated by neutron absorption. Finally, we observed more proton-rich species in experiments containing the non-deuterated H+ donor, corroborating previous studies that indicate that Li-ethoxide may be a major component of the diffuse SEI (Figure 1b-c).5 Compared to LiClO4-based electrolytes, the structure of these LiBF4-derived layers is consistent even after repeated current cycling, indicating a more stable SEI structure and suggesting a relationship between SEI stability and increased Li-NRR performance.References Nørskov, J. et al. DOE Roundtable Report (2016).Chang, F. et al. Advanced Materials 33, 2005721 (2021).Li, S. et al. Joule 6, 2083–2101 (2022).Blair, S.J., Doucet, M. et al. ACS Energy Lett., 7, 6, 1939-1946 (2022).Steinburg, K. et al. Nature Energy, 8, 138-148 (2023). Figure 1: (a) reflectivity curve before and after 4 mins constant applied current at -0.3 mA/cm2 (chronopoteniometry, CP) to plate Li using 1 M LiBF4 with 1 vol% ethanol, (b) resulting scattering length density (SLD) profile, and (c) schematic of layers indicated in SLD profile Figure 1

Gas-Phase Pd Clusters-Modified Mesoporous Copper Oxide Hollow Spheres As Electrocatalysts for CO2 Reduction to Ethylene

Renewable energy-driven electrochemical CO2 conversion to value-added chemicals is a prospective strategy for addressing global carbon emission and energy consumption issues worldwide. Until today, only copper-based electrocatalysts can successfully transform CO2 into C2H4 or other desirable C2+ products, but their stability and product selectivity remain insufficient.1 Oxide-derived Cu mesoporous foam catalysts currently show the best selectivity toward C2+ product formation at particularly low overpotentials due to the availability of specific surface sites for C−C coupling in their structure and to the temporal trapping of gaseous intermediates inside the mesoporous catalyst material during CO2 electrolysis.2 To further improve their stability and product selectivity their surface can be modified with metallic clusters that can promote the adsorption of CO2 and the subsequent formation of intermediates. In particular, Pd clusters provide a favorable surface for the initial adsorption of CO2,3 while inducing a continuous restructuring of the Cu surface that maintains its catalytic properties for CO2 reduction to hydrocarbons.4 Herein, we report a novel highly efficient electrocatalyst for CO2 conversion in C2+ products based on mesoporous oxygen-rich copper hollow spheres prepared by a colloid templating method, whose surface is uniformly modified by the deposition of different loadings of well-defined Pd clusters of ca. 3 nm diameter using the laser ablation cluster beam deposition (CBD) technology.5 Primary electrochemical results show that these electrodes are able to reduce CO2 to ethylene with a faradaic efficiency of more than three times higher than that of commercial Cu2O nanoparticles under the same reaction conditions. A clear phase transition from CuO to Cu2O and metallic Cu is occurring under CO2 electro-reduction conditions as highlighted by XRD.These remarkable performances are likely originating from the facile gas charge transport via the mesoporous structure of the oxygen-rich copper spheres as imaged by SEM (Figure 1) as well as from their high surface area, which allows a high catalytic activity and a uniform accommodation of the metallic clusters.As CBD is a versatile technique that allows the deposition of virtually any type of well-defined cluster on a large variety of support, this work provides an attractive avenue to achieve stable selective multicarbon products via rational electrode design. References Kuhl KP, et al., Energy and Environmental Science, 2012;5 (5):7050-7059.Abhijit Dutta, et al., ACS Catal. 2016, 6, 3804−3814Sichao Ma, et al., J. Am. Soc. 2017, 139, 47−50Zhe Weng, et al., Angew. Int. Ed. 2017, 56, 13135 –13139Chinnabathini V. C., et al., Nanoscale, 2023, DOI: 10.1039/D2NR07287D. Figure 1

Integrated carbon assessment for sludge-derived concrete: Modelling and a comparative study

Concrete, a cornerstone of modern construction, bears a significant responsibility for global carbon emissions due to its energy-intensive pyroprocessing processes and the direct carbon emissions associated with limestone decomposition. With increasing global of awareness environmental sustainability, the imperative to decarbonise concrete production is becoming evident. Recent research highlights the significance of producing eco-friendly "green" concrete by incorporating treated alum sludge, a by-product of drinking water treatment often discarded in landfills, within the framework of sustainable construction. This research endeavours to develop a comprehensive model for the integrated carbon assessment of sludge-derived concrete products. By conducting scenario and sensitivity analyses, the study explores the decarbonisation potentials and quantifies the impacts of diverse variables on carbon output across the entire lifecycle of concrete products. These variables encompass supply chain dynamics, cement replacement ratio, carbon intensity of local grid mix, and the utilization of alternative fuels. Findings reveal that in comparison to general purpose (GP) cement production, sludge treatment has the potential to achieve an impressive 83.48% reduction in carbon emissions. Moreover, a modest 10% substitution of cement with treated sludge would contribute to 9.07% overall carbon reduction. The transition to full renewable energy consumption holds the potential for a remarkable 41.97% carbon reduction, while the integration of photovoltaic (PV) electricity and biogas heating offers a substantial 30.57% carbon reduction in the manufacturing of sludge-derived concrete. Nonetheless, supply chain alterations demonstrate limited impacts, given the prevalence of locally sourced raw materials and the low embodied carbon contents of transportation-intensive ingredients such as sand and aggregate. Despite discernible progress achieved through cement substitution and renewable energy penetration, full carbon neutrality remains an elusive goal.

A dataset of low-carbon energy transition index for Chinese cities 2003–2019

Cities are at the heart of climate change mitigation as they account for over 70% of global carbon emissions. However, cities vary in their energy systems and socioeconomic capacities to transition to renewable energy. To address this heterogeneity, this study proposes an Energy Transition Index (ETI) specifically designed for cities, and applies it to track the progress of energy transition in Chinese cities. The city-level ETI framework is based on the national ETI developed by the World Economic Forum (WEF) and comprises two sub-indexes: the Energy System Performance sub-index, which evaluates the current status of cities’ energy systems in terms of energy transition, and the Transition Readiness sub-index, which assesses their socioeconomic capacity for future energy transition. The initial version of the dataset includes ETI and its sub-indexes for 282 Chinese cities from 2003 to 2019, with annual updates planned. The spatiotemporal data provided by the dataset facilitates research into the energy transition roadmap for different cities, which can help China achieve its energy transition goals.

Trends in Atmospheric CO2 Fertilization Effects with Stand Age Based on Tree Rings

The increase in global carbon emissions has intensified the effects of CO2 fertilization on the carbon cycle. CO2 fertilization is shaped by several factors, including the physiological differences among trees of varied forest ages and types, as well as the influence of different climatic conditions. It is essential to investigate the differences in CO2 fertilization effects across diverse climate zones and delve into the association between these effects and forest age and type. Such exploration will deepen our knowledge of forest responses to environmental changes. This study used annual ring width data from the International Tree-Ring Data Bank, employing the generalized additive mixed models and the Random Forest model to discern the pattern of the CO2 fertilization effect concerning forest age in the Northern Hemisphere. This study also explored the variations in the effect of CO2 fertilization across unique climate zones and the disparities among various forest types within the same climatic zone. The results indicated a link between forest age and the CO2 fertilization effect: it tends to increase in sapling forests and middle-aged forests and diminish in mature forests. Warmer, drier environments had a more marked effect of increased CO2 on tree fertilization. Additionally, coniferous forests demonstrated a more substantial CO2 fertilization effect than broadleaf forests, and deciduous needle-leaf forests surpassed evergreen needle-leaf forests in this regard. This research is pivotal in understanding the shifting patterns of CO2 fertilization effects and how forests respond to atmospheric changes.

Identifying critical transmission sectors, paths, and carbon communities for CO2 mitigation in global supply chains

The critical transmission hubs in global supply chain networks have great potential to reduce carbon emissions, yet this has not received much attention from existing studies. This research combines the structural path betweenness, an extended structural path analysis, and a multi-level modularity optimization algorithm to identify key transmission sectors, paths, and carbon communities in a global supply chain network using the environmentally extended multi-regional input-output analysis. The results show that (1) China's metal industry, power industry, and non-metallic industry are the most crucial transmission centers, and some sectors, such as the paper industry and textile industry in China, are also key transmission hubs, yet they are ignored by traditional accounting methods. From a national viewpoint, China is the most key transmission hub, and its high betweenness is mainly caused by domestic demand. Meanwhile, the CO2 emissions transferred by China to satisfy foreign demand are mainly related to the US, ROW (especially the Asia-Pacific region), and Japan. (2) The transmitted carbon emission is mainly concentrated in the first four production layers, accounting for 69.46 % of the total. (3) The global carbon emission network can be divided into 20 carbon communities. And the largest communities are mainly led by mainland China. Most of the communities contain various national sectors, which offer evidence for international cooperation strategies. The results offer policymakers a new perspective on mitigating environmental stress.

Efficient data-driven prediction of household carbon footprint in China with limited features

Household carbon footprints significantly contribute to global carbon emissions, yet the limited features hinder the accurate assessment in regions with inadequate data. In this study, we developed a data-driven model for evaluating household carbon footprint in China based on a nationwide questionnaire survey. Employing explainable machine learning, we identified the most influential features impacting the model outputs, which were utilized to assess household carbon footprints across Chinese provinces and cities from 2006 to 2021. Additionally, we constructed 27 scenarios with varying peak carbon times, technological evolution pathways, and cumulative carbon budgets to analyze climate change mitigation options for policymakers. The results demonstrate that using the XGBoost-TPE method, the model constructed with only five pivotal features achieves a confidence level of 87.4%. From 2006 to 2021, China's provinces exhibited fluctuating growth trends in household carbon footprints, with economically developed and eastern regions demonstrating higher per capita household carbon footprints. Scenario modelling reveals that combining multiple moderate scenarios yields a 26% increase in effectiveness compared to a single scenario. Promoting low-carbon household food, energy, and transport policies earlier or more aggressively can result in an earlier peak in household carbon footprints and a 2.5% greater carbon reduction.

Optimal Shipping Route under the Designation of the Mediterranean Sulfur Emission Control Area: Mathematical Models and Applications

In order to tackle sulfur oxides (SOx) emissions from maritime activities, both local governmental bodies and the International Maritime Organization (IMO) have implemented a range of regulations with the establishment of sulfur emission control areas (SECAs) being one crucial measure. Recently, the IMO made the significant decision to designate the Mediterranean as an SECA, aiming to promote environmental conservation as well as sustainable development in the maritime industry and mitigate the adverse health effects caused by air pollutants emitted from ships in Mediterranean regions. While this policy signifies significant progress in the reduction of sulfur emissions, it simultaneously presents intricate challenges for maritime enterprises. Notably, under the Mediterranean SECA designation, shipping companies may opt to bypass this region and choose routes through the Cape of Good Hope as a means of minimizing the overall costs, resulting in a potential increase in global carbon emissions. To support shipping companies in formulating optimal strategies within the framework of this new policy, the research introduces advanced techniques to make the optimal decisions concerning route selection, sailing speeds, and the appropriate number of ships for both SECAs and non-SECAs. Furthermore, we elucidate how these optimal decisions can be dynamically adapted in response to the dynamic fluctuations in fuel prices and the weekly operational expenditures incurred by maritime fleets. In the experimental results, taking into account factors like route distance and fuel costs, shipping companies select routes through the Mediterranean region in both eastward and westward directions. The total cost amounts to $6,558,766.78, utilizing eight vessels. Regarding ship speeds, vessels sail at reduced speeds in SECAs compared to non-SECAs. Furthermore, longer voyage distances require deploying a greater number of ships to maintain a weekly service frequency. This research exhibits robust timeliness and practicality, which is in line with practice. It not only timely supplements and enhances the extant body of knowledge concerning SECAs but also serves as a valuable point of reference and emulation for shipping companies seeking to optimize their operations within the framework of the new policy landscape. Furthermore, it offers pertinent insights for the IMO in formulating policies related to SECAs.

Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing

The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.

On Building a Community to Exchange and Share Sustainable Fashion Applications

Sustainable development is a standard plan for peace and prosperity for people and the planet, now and in the future, set forth by the 2030 Agenda for Sustainable Development, which was adopted by all United Nations member states and agreed to in 2015. The Sustainable Development Goals (SDGs) are an urgent call to action for all developed and developing countries in relation to global cooperation. Many factors cause environmental pollution, but the fashion industry is considered by the United Nations Conference on Trade and Development (UNCTAD) as the second most polluting industry in the world. It accounts for 10% of global carbon emissions due to the energy used in production, manufacturing, and transportation. In particular, the factors that strongly affect the environment in the fashion industry are the textile materials, the fabric dyeing industry, and the rapid development of fast fashion. The data was collected through an online survey of 101 fashion consumers and interviews with nine target audiences who need to buy and sell used clothes or are interested in buying and selling used clothes or sustainable fashion. Sustainable design is not a new concept, but it is still quite strange to the Vietnamese. Consumer ignorance about sustainable consumption and the harmful effects of the fashion industry on planet Earth is a big reason why consumers constantly plunge into the shopping vortex. The problem of wasteful and unsustainable fashion consumption is a big global problem at a time when the fashion industry is gradually emerging into the sustainable fashion movement. This is an opportunity to build a community by connecting consumers through their essential needs, helping them raise awareness, change their behavior, and make each individual part of a sustainable fashion community. They are thus contributing to sustainable development in the future.
 Keywords: SDGs, sustainable fashion, sustainable consumption, community

Framing the path to net zero

Abstract Big corporations are a leading contributor to global carbon emissions and their investment decisions have a significant impact on the world’s ability to tackle climate change. This study combines corpus and discourse approaches to examine how major corporate emitters have responded to the Paris Agreement, how they legitimize their practices amid mounting public pressure, and how companies operating in high- and middle-income countries differ in their framing of climate change. The results show that carbon majors place increasing focus on climate issues, widely support the goals of the Paris Agreement, and are increasingly making net-zero pledges. However, close inspection of linguistic patterns reveals a troubling disconnect between proclaimed goals, the solutions advocated for, and the radical steps needed to address the escalating climate crisis. Companies from middle-income countries devote comparatively less attention to climate change, which points to the need for better coordinated global efforts to address this problem.

Determinants of global carbon emission and aggregate carbon intensity: A multi-region input−output approach

A deep understanding of the drivers behind aggregate carbon indicators, including carbon emissions and carbon intensity, is essential for achieving low-carbon economic growth. However, integrated analysis incorporating both indicators remains limited. Using a multi-region input-output model and structural decomposition analysis, we examine the determinants behind the changes in carbon indicators during 2000–2014. Our findings reveal the efficacy of global carbon reduction strategies, characterized by a decline in total emissions increase and an accelerated reduction in carbon intensity. Further decomposition shows that the carbon efficiency effect is the primary factor in reducing the carbon indicators by improving sectoral direct carbon intensity. The input structure effect is the second largest contributor, mainly resulting from the decarbonization of intermediate inputs in the supply chain. The final demand effect is the main impediment due to the steady growth of consumption and investment demand across economies.

EU-Russia energy decoupling in combination with the updated NDCs impacts on global fossil energy trade and carbon emissions

The updated nationally determined contributions (NDCs) and EU-Russia energy decoupling have profound impacts on global fossil energy trade and carbon emissions. This study utilizes a dynamic general equilibrium model (GDyn-E) and conducts scenario analysis to illustrate the evolving patterns of global fossil energy trade and carbon emissions in light of the updated NDCs and EU-Russia energy decoupling. A sharp decrease in global fossil fuel consumption will reduce the global volume of fossil energy trade. Major fossil energy trade flows will divert from Europe to Asia, particularly China and India. EU-Russia energy decoupling directly triggers adjustments in the global fossil energy trade such that the EU imports more fossil energy from the Middle East, Norway, and the United States, while Russian fossil energy exports are steered toward Asia. This shift in energy trade patterns is expected to lead to a reduction of approximately 2 Gt CO2 in cumulative carbon emissions worldwide by 2050, which is primarily driven by the intensified efforts of European countries in transitioning to renewable energy sources. The findings of this study offer valuable insights into the interplay between geopolitical conflicts, fossil energy trade dynamics, the updated NDCs, and carbon emission reduction.

Development Status and Management Experience of Corporate Carbon Management in the Dual Carbon Background: A Case Study of Huayin Power

In order to address global environmental issues such as climate change, countries around the world have introduced climate change policies, such as the Kyoto Protocol and the Paris Agreement, to limit global carbon emissions. China has actively responded to climate change and other political and economic issues and proposed the "Dual Carbon" strategy in 2020, vigorously developing clean energy and promoting the green and low-carbon transformation of industries. This paper analyzes the impact of "carbon trading" on the "carbon management" of Hua Yin Electric Power from the perspective of ESG analysis, focusing on the environmental, social, and corporate governance aspects. The study aims to summarize the management experience of Hua Yin Electric Power in "carbon management" and provide reference for further optimizing and improving the "carbon management" in the power industry. Hua Yin Electric Power, as a leading company in the power industry, is representative and has reference significance for the optimization and improvement of "carbon management" in the power industry.

Application of Artificial Intelligence in Carbon Accounting and Firm Performance: A Review Using Qualitative Analysis

The increasing role of the corporate sector in global carbon emissions has raised significant concern among stakeholders, leading to a thorough reassessment of corporate responsibility. Corporations, directly influencing society due to their carbon emissions, acknowledged as externalities, are facing growing pressure to tackle this environmental challenge actively. As a reaction, companies are increasingly implementing carbon accounting and other environmentally conscious practices in line with a broader dedication to comprehensive environmental ethics. The accounting department, tasked with incorporating carbon accounting into non-financial reports alongside regular financial statements, encounters adoption obstacles such as limited awareness, insufficient expertise, unclear guidelines, and a lack of standardized terminology. This paper conducts a comprehensive study to evaluate the feasibility of using artificial intelligence (AI) solutions in carbon accounting and its subsequent effect on firm performance based on the well-established effectiveness of AI in financial reporting and accounting tasks. As of 2021, Saudi Aramco, the foremost emitter of carbon dioxide worldwide, accounts for more than 4 percent of global Greenhouse Gas Emissions. This highlights the pressing necessity for intervention in this industry. Moreover, the study highlights electricity and heat production as the primary global sources of carbon emissions. The research demonstrates a clear link between effective carbon accounting practices and improved firm performance, especially when stakeholders are highly aware of sustainability issues. Nevertheless, the paper highlights the considerable obstacles and the urgent requirement for extensive advancement in AI applications to fully exploit its potential as a significant resolution to the complexities of carbon accounting. The study is limited by the wide range of accounting standards companies use to disclose their environmental impact and a need for more information on using artificial intelligence in carbon accounting. The research highlights the crucial significance of collaborative endeavours in promoting AI solutions to strengthen carbon accounting practices, thereby enhancing corporate sustainability and overall performance.

Unraveling the enigma of NPP variation in Chinese vegetation ecosystems: The interplay of climate change and land use change

Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil–Sen median trend analysis and the Mann–Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m−2·year−1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m−2·year−1), followed by cropland ecosystem (CES) (4.00 gC·m−2·year−1) and grassland ecosystem (GES) (3.40 gC·m−2·year−1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.

5G on the Farm: Evaluating Wireless Network Capabilities and Needs for Agricultural Robotics

Global food security is a critical issue today, strained by a wide range of factors including global warming, carbon emissions, sociopolitical and economic challenges, traditional workforce decline and population growth. Technical innovations that address food security, like agricultural robotics, are gaining traction in industry settings, moving from controlled labs and experimental test facilities to real-world environments. Such technologies require sufficient network infrastructure to support in-field operations; thus, there is increased urgency to establish reliable, high-speed wireless communication networking solutions that enable deployment of autonomous agri-robots. The work presented here includes two contributions at the intersection of network infrastructure and in-field agricultural robotics. First, the physical performance of a private 5G-SA system in an agri-robotics application is evaluated and in-field experimental results are presented. These results are compared (using the same experimental setup) against public 4G and private WiFi6 (a newly emerging wireless communication standard). Second, a simulated experiment was performed to assess the “real-time” operational delay in critical tasks that may require quick turnaround between in-field robot and off-board processing. The results demonstrate that public 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication; that private 5G-SA greatly outperforms public 4G in all performance metrics (as expected); and that private WiFi6, though limited in range, is a fast and very reliable alternative in specific settings. While a single wireless solution does not currently exist for the agricultural domain, multiple technologies can be combined in a hybrid solution that meets the communications requirements.

Electrocatalytic CO2 Reduction to C2+ Products in Flow Cells.

Electrocatalytic CO2 reduction into value-added fuels and chemicals by renewable electric energy is one of the important strategies to address global energy shortage and carbon emission. Though the classical H-type electrolytic cell can quickly screen high-efficiency catalysts, the low current density and limited CO2 mass transfer process essentially impede its industrial applications. The electrolytic cells based on electrolyte flow system (flow cells) have shown great potential for industrial devices, due to higher current density, improved local CO2 concentration, and better mass transfer efficiency. The design and optimization of flow cells are of great significance to further accelerate the industrialization of electrocatalytic CO2 reduction reaction (CO2 RR). In this review, the progress of flow cells for CO2 RR to C2+ products is concerned. Firstly, the main events in the development of the flow cells for CO2 RR are outlined. Second, the main design principles of CO2 RR to C2+ products, the architectures, and types of flow cells are summarized. Third, the main strategies for optimizing flow cells to generate C2+ products are reviewed in detail, including cathode, anode, ion exchange membrane, and electrolyte. Finally, the preliminary attempts, challenges, and the research prospects of flow cells for industrial CO2 RR toward C2+ products are discussed.

Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.