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This paper aims to introduce a new sustainable business excellence model, called Knowledge Management Excellence Model 21 (KMEM21), a theoretical framework that combines Knowledge Management and Business Excellence Models (BEMs), proposing an approach applicable to organizations to directly contribute towards achieving the United Nations Sustainable Development Goals (SDGs). The paper through a qualitative-exploratory methodology makes an analysis and benchmarking, emphasizes the integration of Knowledge Management and three specific Business Excellence Models (Management Excellence Model (MEG) from Brazil, Baldrige Excellence Framework, and EFQM). This integration uncovers relationships between organizational concepts associated with Knowledge Management and BEMs, which contributes to new insights in the literature regarding these concepts' interplay. BEMs are presented as tools that can catalyze the power of Knowledge Management towards achieving SDGs. However, it's noted that the existing BEMs might not be adequate, as the goal extends beyond organizational improvement and excellence to encompass sustainable development aligned with SDGs. The article suggests that specific organizational concepts, identified through a literature review and aligned with Knowledge Management and BEMs, can be selected as indicators associated with the constructed KMEM21 and the SDGs. These indicators are intended to form a bridge between the new model and the SDGs. However, given the comprehensive nature of the theory, model development, application, result acquisition, and analysis, the article's primary contribution will be in its focus on elaborating the instrument and technique, demonstrating the creation of a theoretical framework for the SDGs. Future works will showcase the application of the proposed KMEM21 method in five Brazilian organizations, members of Global Compact, to compare the achieved objectives and the results obtained with the organizations' performance and their sustainability reports. [Display omitted] • There is a visible relationship between knowledge management and business excellence models, aided by shared organizational concepts. • Achieving the SDGs in organizations can be done by using the traits incorporated into knowledge management and business excellence models. • The Knowledge Management Excellence Model 21 represents an original approach, acting as a new sustainable business excellence model. • A comprehensive model can be constructed using a methodology that incorporates an instrument, a technique, and a method. • KMEM21 presents a strategic path that aligns with business excellence principles and the sustainable development objectives, offering a framework for organizational success. [ABSTRACT FROM AUTHOR]

Tropical climate-smart agriculture could be a cost-effective and scalable nature-based solution toward achieving net-zero greenhouse gas (GHG) emissions in the coming decades. Nevertheless, the lack of integrated on-farm CO 2 , CH 4 , and N 2 O flux measurements remains a barrier to including these systems in certification programs and the carbon (C) market. This systematic literature review critically examines field-measured GHG emission studies within climate-smart agri-systems in Brazil, aiming to: (i) integrate GHG-measured data, (ii) explore geographical representativeness and connections between organizations and authors, (iii) analyze distributions of studied GHG molecules, climate-smart system types, and productive components, (iv) conduct a meta-analysis on the adoption of climate-smart agriculture, and (v) identify methodological and scope gaps, providing insights for future research directions. The systems considered include no-till, well-managed pasture, integrated crop-livestock, and integration with trees (i.e., crop-forestry, livestock-forestry, crop-livestock-forestry, and multistrata agroforestry). Out of 1200 papers mentioning GHGs and climate-smart systems, 74 contained reliable on-farm flux measurements. Most authors were Brazilians affiliated with Embrapa and public Universities. Studies concentrated on the Atlantic Forest and Cerrado, with scarce data for other biomes. Over half of the trials assessed individual GHG molecules and only one climate-smart system. Urochloa spp. and Avena spp. were the most studied forage genera, while soybeans and corn dominated annual crops. Eucalyptus spp. and beef cattle were common trees and animals. No-till, integrated crop-livestock, and agricultural integrations involving trees showed soil C–CH 4 influx from 0.8 to 1.0 kg ha−1 y−1. Transitioning from pasture monoculture to integrated systems decreased N–N 2 O emissions by up to 1.63 kg ha−1 y−1. Converting extensive grazed land to intensified-integrated systems reduced animal CH 4 intensity, with cuts of up to 122 g CH 4 per kg of average daily weight gain. Future research must urgently address methodological issues, such as de-standardization (e.g., 8–10 flux units found per GHG molecule) and the absence of crucial ancillary variables. In conclusion, adopting diversified climate-smart agri-systems shows potential to mitigate GHG emissions, but caution is advised when extrapolating our numerical findings due to limited data across Brazilian edaphoclimatic conditions. We urge scientists to prioritize frequent on-farm measurements of all three GHGs (CO 2 , N 2 O, and CH 4) along with agroecosystem C sequestration across multiple climate-smart systems, enabling reliable and comparable net GHG flux calculations to identify the most sustainable option. Advancing and standardizing GHG measurements in Brazil offer an opportunity to enhance GHGs inventory accuracy, calibrate models, and promote policies and investments that expand refined tropical climate-smart agriculture. [Display omitted] • We systematically reviewed GHG measurements in Brazilian climate-smart agri-systems. • Well-managed pastures, no-till, and integration-based systems mitigate GHG emissions. • Brazilian GHG dataset incompletely represents systems and environmental complexities. • Producing agri-GHG field data is still vital for policies, investments, and C market. • Future GHG research should standardize flux units and include ancillary variables. [ABSTRACT FROM AUTHOR]

Two economic activities have been integrated in southern Brazil: olive cultivation and sheep farming. The favorable edaphoclimatic conditions in the southern region for cultivating olive trees and cultural preferences for the consumption of sheep meat make this integration a valuable strategy. Considering that every activity causes environmental impact and the exploitation of natural resources, new systems that promote sustainable development and the efficient use of these resources become necessary. This paper presents the first study of the carbon footprint of the life cycle of integrating of sheep and olive farming, with the objective to build an inventory based on primary data collected on olive and sheep farms in Brazil, determine the carbon footprint of the conventional and integrated system and estimate the potential for mitigating GHG emissions. The evaluation was performed using the life cycle assessment (LCA) methodology. The study focuses on the detailed analysis of the processes and activities that are responsible for GHG emissions, including enteric methane emissions, methane emissions from manure management, emissions from nitrogen fertilizer use and limestone application and energy consumption. For this, two major scenarios were considered, the first centered around on the traditional olive cultivation and sheep farming systems individually, and the second involved their synergistic integration. The findings indicate that the conventional system is associated with a global warming impact of 6.8 kg CO 2 eq per liter olive oil and kg live weight. In contrast, the integrated system demonstrates a significant mitigation potential of 27.7%, resulting in a reduced global warming impact of 4.92 kg CO 2 eq. This advantage is a consequence of the synergy between the systems, as they now occupy the same area, which allows the sharing of agricultural activities and agrochemical inputs, in addition to optimizing nutrient recycling and complementing each other. Additionally, the results of a sensitivity analysis showed that enteric methane from sheep and the application of limestone to the soil are the most influential parameters on the carbon footprint, indicating that they are key parameters for the development of GHG mitigation strategies, some of which were discussed in the article. • First life cycle assessment of integration between olive and sheep farming in Brazil. • The inventory was constructed with primary data collected in situ. • The study assesses the carbon footprint of the olive and sheep farming system. • The results show environmental advantages of the integration of olive and sheep farming systems. [ABSTRACT FROM AUTHOR]

Pastureland maintenance is seen as a land-based measure to reduce dependency on feed concentrates and mitigate greenhouse gas (GHG) emissions from livestock production in the EU, while providing other ecosystems services. This paper assesses potential market-mediated impacts, including global Land Use Change (LUC) and GHG emissions, from increased subsidies to pasture-based livestock production in the EU. A tax recycling strategy (TRS) is simulated against a baseline up to 2030 under the shared socioeconomic pathway 2 (SSP2). This implies a budget-neutral increase in the level of pasture subsidies in individual Member States, as land subsidies for other cropping activities decrease. We employ the computable general equilibrium (CGE) model GTAP in its recursive-dynamic version, GTAP-RDEM, extended with the Multi-Regional Input-Output (MRIO) database FABIO to disaggregate agri-food sectors from 21 to 31. This approach allows considering price- and income-dependent feedbacks when assessing long-run changes in the global economy, improving the sectoral resolution relative to GTAP v10. The policy increases pastureland areas and cattle production in almost all EU Member States, whereas cropland and crop production decrease, causing significant changes across EU agri-food markets. Crop prices increase, leading to the reduced output of intensive animal production sectors, mainly pig and poultry. Cropland areas decrease and most EU countries increase imports of grain, oilseeds, and cakes, essentially soybean cake from Brazil and North America. While GHG emissions decrease in those EU countries where pasturelands expand mainly at the cost of croplands, GHG emissions increase in those countries where pastureland expansion comes with forest loss. As a result, net GHG emissions increase in the EU-27 in 2030 (+2.49 Mt CO 2 -eq). Emissions from LUC in major non-EU grain- and oilseed-exporting countries increase, e.g., by 102.52 Mt CO 2 -eq in Brazil and by 129.17 Mt CO 2 -eq in North America. The simulated policy shows that promoting extensive livestock per se does not meet the objectives of the Common Agricultural Policy and the EU Green Deal. The TRS should be complemented with policies to foster crop diversification and promote the use of domestic feed sources (e.g., legumes) to effectively ensure feed self-sufficiency and that extensive cattle production in the EU does not lead to deforestation in carbon-rich countries. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) plays a strategic role in transitioning from a fossil to a renewable energy matrix, encouraging the reduction of energy poverty and access to green energy in traditional communities. This paper proposes a statistical model based on structural equations to analyze the influence of DG on Well-Being (CWB), and to estimate the causal relationships between the latent variables: economic (ECO), environmental (ENV), social (SOC), distributed generation system (DGS) and well-being (CWB) in a rural area in Brazil. The research is carried out in a Quilombola Community located in the south of Brazil. In the data collection process, a questionnaire was used in a Likert scale format, applied in loco to the residents of the Quilombola community. This database was imported into the SmartPLS software for analysis using a partial least squares approach. Structural equation-based modelling (SEM) results confirmed 66% of the hypotheses. The latent variables SOC and DGS directly and positively affected CWB with (β = 0.573) and (β = 0.433), respectively. The Economic variable with (β = 0.396) had a direct and positive effect on the DGS variable, and the DGS variable with (β = 0.404) had an immediate and positive impact on the CWB. The variables observed with the highest participation in the latent variables were: belonging (62%), social capital (31%), greenhouse gases (56%) and incentive policies (45%). The proposed model demonstrates that socioeconomic changes, carried out from specific public policies around the installation of DG, should contribute to the well-being of traditional communities and the reduction of energy poverty. • An analysis of the well-being development of a rural community was done. • For data collection process was used a Likert scale. • Only the social dimension had a direct effect on the well-being of the community. • The designed model demonstrated that the variables are susceptible to changes, influencing on public policies. • DG promotes well-being beyond technical issues and should encourage its installation in rural communities. [ABSTRACT FROM AUTHOR]