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The objective of this study was to examine the impact of the pandemic on sustainable agricultural practices (SAP) adoption such as: organic fertilizers, minimal use of tillage, crop rotation, soil burning and crop association in the frame of family farming systems in Ecuador.

The present research employed probit models' estimation with pooled data from 2018 to 2020. The study combined three sources of information with The Survey on Surface and Agricultural Continuous Production, as the main. This study also proposed the analysis of six regions: Coast, Coast Mountains, Northern Highlands, Central Highlands, Southern Highlands and the Amazon.

The authors see a lower adoption in the year 2020, where the pandemic was one of the causes. The only exception was the use of organic fertilizer. The adoption of these sustainable practices differed across the six regions. The findings also reveal that the employment generated by agricultural enterprises had a negative influence on the adoption of three sustainable practices, and that for the remaining practices the effect was positive.

The data set lacks information on the acceptance and the application of the practices promoted by agricultural technical assistance, which could provide insights into the effectiveness of the learning process. The limited observation period does not allow for investigating long-term effects on sustainable practices adoption.

This study helps to understand the implications of the COVID-19 pandemic in the adoption of SAP. Additionally, this research can help with the scalability of the practices starting from the regions that are most likely to adopt each of them.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

The sustainable development goals (SDGs) are introduced to guide achieving the sustainable goals and tackle the global problems. United Nations members may perform activities to achieve the predetermined goals and report on their SDG activities. The comprehension and commitment of several stakeholders are essential for the effective implementation of the SDGs. Countries encourage their stakeholders to perform and report their activities to meet the SDGs. The purpose of this study is to investigate the extent to which corporations’ annual reports address the SDGs to assess and comprehend their level of commitment to, priority of and integration of SDGs within their reporting structure. This research makes it easier to evaluate corporations’ sustainability performance and contributions to global sustainability goals by looking at the extent to which they address the SDGs.

In the study, it is revealed to what extent the reports meet the SDGs with the multilabel text classification approach. The SDG classification is carried out by examining the report with the help of a text analysis tool based on an enhanced version of gradient boosting. The implementation of a machine learning-based model allowed it to determine which SDGs are associated with the company’s operations without the requirement for the report’s authors to perform so. Therefore, instead of reading the texts to seek for “SDG” evidence as typically occurs in the literature, SDG proof was searched in relevant texts.

To show the feasibility of the study, the annual reports of the leading companies in Turkey are examined, and the results are interpreted. The study produced results including insights into the sustainable practices of businesses, priority SDG selection, benchmarking and business comparison, gaps and improvement opportunities identification and representation of the SDGs’ importance.

The findings of the analysis of annual reports indicate which SDGs they are concerned about. A gap in the literature can be noticed in the analysis of annual reports of companies that fall under a particular framework. In addition, it has sparked the idea of conducting research on a global scale and in a time series. With the aid of this research, decision-making procedures can be guided, and advancements toward the SDGs can be achieved.

The authors revisit the notion of co-production, highlight more critical and re-politicized forms of co-production and introduce three principles for its operationalization. The paper’s viewpoint aims to find entry points for enabling more equitable disaster research and actions via co-production.

The authors draw insights from the authors’ reflections as climate and disaster researchers and literature on knowledge politics in the context of disaster and climate change, especially within critical disaster studies and feminist political ecology.

Disaster studies can better contribute to disaster risk reduction via political co-production and situating local and Indigenous knowledge at the center through three principles, i.e. ensuring knowledge plurality, surfacing norms and assumptions in knowledge production and driving actions that tackle existing knowledge (and broader sociopolitical) structures.

The authors draw out three principles to enable the political function of co-production based on firsthand experiences of working with local and Indigenous peoples and insights from a diverse set of co-production, feminist political ecology and critical disaster studies literature. Future research can observe how it can utilize these principles in its respective contexts.

Agriculture in Ethiopia relies heavily on traditional farm power sources and is designated by the lowest farm machinery access, in contrast to other Sub-Sahara African (SSA) countries. The purpose of this research is to analyze the heterogeneity of mechanization service transactions and factors determining farmers' cooperation in mechanization clusters and willingness to accept land consolidation.

The authors conducted a cross-sectional survey of producer households in major crop production areas in the Oromia, SNNPR, Amhara and Tigray regions. The sampling design involved three stages: districts were selected using a stratified sampling approach accompanied by simple random samples of kebele units and producer households in the second and final stages, respectively.

This study’s results show that mechanization service costs, service relationships, clustering and land consolidation exhibit significant heterogeneity across the study areas. Cluster farming was found to be advantageous against diseconomies, rationalized by upgrading the mechanization scale. The probit model parameterization of the probability distributions reveals that household, land, crop, mechanization service, remoteness and location-related factors determine participation in mechanization clusters and willingness to accept land consolidation.

Fostering cooperation by focusing on constraints and demand of users is suggested to reduce transaction costs and expand hired mechanization services to unaddressed areas. The findings are relevant to most SSA countries where mechanization development is hampered by land fragmentation.

Limited information is available on agricultural mechanization development for smallholder farmers, particularly in Ethiopia, and this study adds empirical evidence about the synergy between cluster farming and mechanization, horizontal coordination and alternative supply models.