Using transfer learning-based plant disease classification and detection for sustainable agriculture

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's "Zero Hunger," "Climate Action," and "Responsible Consumption and Production" sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

Ecological footprint in the OECD countries: do energy efficiency and renewable energy matter?

Ecological footprint (EFP) measures the amount of area, that is land or sea, which is required to absorb the waste generated through human activities or to support the production of resources consumed by populations. EFP index therefore includes six dimensions that are cropland, forestland, carbon, fishing grounds, grazing land, and built-up area. Human activities have impacted the environment, leading to global warming, widespread droughts, and diseases. The present study aims to investigate the role of renewable energy (RE) and energy efficiency on the EFP index. Past researchers have widely used carbon emission (CE) to represent environmental impact, and recent studies have shown that EFP index is a better proxy of environmental degradation. Therefore, the present research differs from past studies in that it compares on how the determinants of environmental degradation affects EFP index and CE. Panel dataset of the OECD countries from 1990 to 2020 is employed. The CS-ARDL, DCCEMG, and AMG techniques, which overcome dynamics, heterogeneity, and cross-sectional dependence, are employed. The main findings depict that RE significantly reduces EFP and CE, while economic growth significantly exacerbates them. Energy efficiency reduces CE, but does not significantly affect EFP. Non-renewable energy and research & development significantly increase CE, while an insignificant positive effect is observed with EFP. This paper shows that factors that significantly influence CE may not always significantly affect the EFP index. Thus, to reduce environmental degradation it is fundamental to understand on how each dimension of EFP is influenced.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

An exploratory study of women and midwives' perceptions of environmental waste management - homebirth as climate action

BACKGROUND

Inspired by observing midwives working with birthing women in Bali and at homebirths in Australia, this study explores the meanings associated with environmental waste at birth.

AIMS

The aim is to better understand how and why women and midwives from the homebirth community in Australia choose to manage waste generated during the birthing process. Babies across the globe are born without a carbon footprint and are united, no matter their location, by a future that will require an understanding of and action against climate change.

METHODS

This qualitative exploratory study investigated midwives' (n = 10) and women's (n = 10) perspectives on environmental waste generated from birth at home. Data were collected through semi-structured interviews and analysed thematically.

RESULTS

Three overarching themes were identified from the data. The first theme "There is minimal waste from birth at home" demonstrates participants' perception of the difference in waste generated by birth at home compared to birth in a hospital. The second theme, "Organic waste from homebirth is beneficial to the environment," spoke to participants' embeddedness and connections within their surrounding community environment. The third theme, "Formal education around managing waste at homebirth doesn't exist," indicates a lack of structured or official education or training programs available to individuals interested in learning about sustainable waste management practices during home birth.

CONCLUSION

Birthing at home has a low environmental impact as clinical waste is negligible. This research demonstrates a need to incorporate sustainable waste management into midwifery education while respecting midwifery practices in the home setting.

Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.

Sustainable Development Goals for Textiles and Fashion

In this paper, each of the 17 Sustainable Development Goals (SDGs) is discussed in the context of textiles and fashion. The necessity of collaborative efforts is accentuated to overcome the climate crisis and human health concerns encountered by the textiles and fashion industry. The concerns over poverty faced by cotton farmers, soil depletion, and toxicity to aquatic life due to microfibres and heavy metals are presented. The paper underlines numerous corrective practises such as the utilisation of African Organic Cotton, Better Initiative Cotton, and others that could enable curtailing poverty and hunger. The requirement for a more corporate and socially responsible textiles and fashion business that would propel SDGs is described with inspiring examples of Inditex, Culthread, Saint Basics, Flamingo's Life, etcetera. More, the greenwashing and ardent necessity of transparency across the fashion value chain is emphasised herein. The importance of reducing inequalities and working in partnership for innovation and justice is highlighted such as apple leather, algal foam, and others. Even more, the production waste and landfill disputes are reviewed. Eventually, the paper concludes with an appeal for mindful and diligent efforts from textiles and fashion consumers, designers, manufacturers, and traders to continually adapt to SDGs even after 2030 as there is no planet B. Also, abiding by the laws of nature as listed herein is found to be the key to reaching SDGs.

Non-monetary narratives motivate businesses to engage with climate change

The dominant narrative to motivate business actors to take climate actions emphasizes opportunities to increase monetary gains, linking sustainability to the financial goals of these organizations. The prevalence of monetary motivations in sustainability communication among businesses, consultancies, academics and international organizations has made this narrative a truism in the private sector. We conducted an online, real-world, large-n experiment to evaluate the comparative effectiveness of different motivations using narrative communication. We show that non-monetary narratives highlighting prosocial or achievement motivations are 55% more effective in creating responses from businesses than narratives emphasizing monetary gains. These findings are robust across most narrative and audience characteristics, including age and language. Our findings suggest that communication towards business leaders around sustainability can be multi-pronged and should incorporate prosocial and achievement motivations aside from articulating potential financial benefits.

Impact of climate change on groundwater recharge in shallow young glacial aquifers in northern Poland

We investigated the influence of climate change in the period 1951-2020 on shallow aquifers in the Brda and Wda outwash plains (Pomeranian Region, Northern Poland). There was a significant temperature rise (0.3 °C/10 years), which accelerated after 1980 (0.66 °C/10 years). Precipitation became increasingly irregular - extremely rainy years occurred right after or before extremely dry years, and intensive rainfall events became more frequent after 2000. The groundwater level decreased over the last 20 years, even though the average annual precipitation was higher than in the previous 50 years. We carried out numerical simulations of water flow in representative soil profiles for the years 1970-2020 using the HYDRUS-1D model, developed and calibrated during our earlier work at an experimental site in the Brda outwash plain (Gumuła-Kawęcka et al., 2022). We used a relationship between the water head and flux at the bottom of the soil profiles (the third-type boundary condition) to reproduce groundwater table fluctuations caused by recharge variability in time. The calculated daily recharge showed a decreasing linear trend for the last 20 years (0.05-0.06 mm d^-1/10 years), and dropping trends in water table level and soil water content in the entire profile of vadose zone. Field tracer experiments were performed to estimate impact of extremely rain events on water flux in vadose zone. The results suggest that tracer travel times are strongly determined by water content in the unsaturated zone which is determined by precipitation amount in span of weeks, rather than extremely high precipitation events.

CO2 uptake by activated hydrochar derived from orange peel (Citrus reticulata): Influence of carbonization temperature

In this study, activated hydrochar was prepared from orange peel (OP) waste using KOH for the first time for potential environmental applications. The influence of hydrothermal carbonization temperature (180 °C, 200 °C, and 220 °C) on the CO₂ adsorption capacity of OP-derived activated hydrochar (OP-180, OP-200, and OP-220) was investigated. Scanning electron microscope (SEM) images revealed that the activated OP hydrochar has high microporosity, a desired attribute for effective adsorption. The yield and the oxygen content of the hydrochar decreased with the increasing process temperature whereas the carbon content showed an increase. Fourier-transform infrared spectroscopy showed the presence of various functional groups including ketone, aldehydes, esters, and carboxyl in the hydrochar. CO₂ adsorption isotherm was determined for all hydrochar samples. At 25 °C and 1 bar, OP-220 showed the highest CO₂ uptake at 3.045 mmol/g. The use of OP waste for CO₂ adsorption applications contributes to carbon neutrality and a circular economy.

COVID-19 response in Africa: impacts and lessons for environmental management and climate change adaptation

The COVID-19 pandemic adds pressure on Africa; the most vulnerable continent to climate change impacts, threatening the realization of most Sustainable Development Goals (SDGs). The continent is witnessing an increase in intensity and frequency of extreme weather events, and environmental change. The COVID-19 was managed relatively well across in the continent, providing lessons and impetus for environmental management and addressing climate change. This work examines the possible impact of the COVID-19 pandemic on the environment and climate change, analyses its management and draws lessons from it for climate change response in Africa. The data, findings and lessons are drawn from peer reviewed articles and credible grey literature on COVID-19 in Africa. The COVID-19 pandemic spread quickly, causing loss of lives and stagnation of the global economy, overshadowing the current climate crisis. The pandemic was managed through swift response by the top political leadership, research and innovations across Africa providing possible solutions to COVID-19 challenges, and redirection of funds to manage the pandemic. The well-coordinated COVID-19 containment strategy under the African Centers for Disease Control and Prevention increased sharing of resources including data was a success in limiting the spread of the virus. These strategies, among others, proved effective in limiting the spread and impact of COVID-19. The findings provide lessons that stakeholders and policy-makers can leverage in the management of the environment and address climate change. These approaches require solid commitment and practical-oriented leadership.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-02956-0.

Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter

Anthropogenic activities over the past few decades have led to increased vulnerability of environmental and ecological stability on this planet. Accelerated climate change is one such subset of the environmental problems that threatens the very existence of humankind in twenty first century. Governments, United Nations (UN) and other humanitarian agencies across the globe have developed and devised strategies for climate action that requires grater public awareness and actions. Social media has played a vital role in information dissemination and raising public awareness of climate change in the digital era. To this aid, an upsurge has been documented in recent times regarding discussions over climate change with #SDG13 (Sustainable Development Goals) at its epicenter. Following the principles of Actor Network Theory (ANT) we analyzed a large volume of Twitter data to understand general citizens' perception and attitude towards climate change. Our findings unveil people's opinion on causes and concerns related to barriers of adopting a more sustainable consumption and lifestyle practice. There is also a growing apathy towards sluggish government actions that makes little difference. People were also found to exchange innovative concepts and measures towards mitigating the effects of climate change.

The effects of COVID-19 transmission on environmental sustainability and human health: Paving the way to ensure its sustainable management

The transmission dynamics and health risks of coronavirus disease 2019 (COVID-19) pandemic are inextricably linked to ineract with environment, climate, air pollution, and meteorological conditions. The spread of COVID-19 infection can thus perturb the 'planetary health' and livelihood by exerting impacts on the temporal and spatial variabilities of environmental pollution. Prioritization of COVID-19 by the health-care sector has been posing a serious threat to economic progress while undermining the efforts to meet the United Nations' Sustainable Development Goals (SDGs) for environmental sustainability. Here, we review the multifaceted effects of COVID-19 with respect to environmental quality, climatic variables, SDGs, energy resilience, and sustainability programs. It is well perceived that COVID-19 may have long-lasting and profound effects on socio-economic systems, food security, livelihoods, and the 'nexus' indicators. To seek for the solution of these problems, consensus can be drawn to establish and ensure a sound health-care system, a sustainable environment, and a circular bioeconomy. A holistic analysis of COVID-19's effects on multiple sectors should help develop nature-based solutions, cleaner technologies, and green economic recovery plans to help maintain environmental sustainability, ecosystem resilience, and planetary health.

Crucial factors of the built environment for mitigating carbon emissions

Global warming and environmental changes are becoming increasingly threatened by carbon emissions, especially in urban areas. Low-carbon cities have the co-benefits of mediating environmental threats and lowering carbon emissions. However, the direct and indirect pathways and effects between the built environment and carbon emissions remain unclear, limiting low-carbon city development. Therefore, this study used partial least squares (PLS) modeling and urban-scale data from nineteen counties in Taiwan to identify the crucial effects and pathways affecting carbon emissions. The model considered the impacts of the characteristics of urban form (i.e., density, land mix, city size, urban sprawl, and jobs-housing balance), urban function (i.e., industrial and commercial levels), urban transportation, and urban greening on carbon emissions. The results reveal that minimizing city size, urban sprawl, industrial level, and transportation status, and maximizing density, land mix, commercial levels, and urban green coverage could reduce carbon emissions. This is the first study to apply PLS modeling to identify variable pathways and evaluate both direct and indirect effects of built environment characteristics on carbon emissions. Findings demonstrated that appropriate urban policies and planning, such as compact cities, green cities, or transit-oriented development, might lower carbon emissions and thus further serve as useful strategies for building low-carbon cities.

A hybrid approach to identifying and assessing interactions between climate action (SDG13) policies and a range of SDGs in a UK context

In 2015 the United Nations drafted the Paris Agreement and established the Sustainable Development Goals (SDGs) for all nations. A question of increasing relevance is the extent to which the pursuit of climate action (SDG 13) interacts both positively and negatively with other SDGs. We tackle this question through a two-pronged approach: a novel, automated keyword search to identify linkages between SDGs and UK climate-relevant policies; and a detailed expert survey to evaluate these linkages through specific examples. We consider a particular subset of SDGs relating to health, economic growth, affordable and clean energy and sustainable cities and communities. Overall, we find that of the 89 UK climate-relevant policies assessed, most are particularly interlinked with the delivery of SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) and that certain UK policies, like the Industrial Strategy and 25-Year Environment Plan, interlink with a wide range of SDGs. Focusing on these climate-relevant policies is therefore likely to deliver a wide range of synergies across SDGs 3 (Good Health and Well-being), 7, 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure), 11, 14 (Life Below Water) and 15 (Life on Land). The expert survey demonstrates that in addition to the range of mostly synergistic interlinkages identified in the keyword search, there are also important potential trade-offs to consider. Our analysis provides an important new toolkit for the research and policy communities to consider interactions between SDGs, which can be employed across a range of national and international contexts.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43621-021-00051-w.

From values to climate action

To mitigate anthropogenic climate change, it is important to know what motivates individuals to support and take climate action. Values reflect universal, general, desirable goals which guide individuals' preferences and actions. Stronger biospheric values (caring about the environment), in particular, predict stronger engagement in climate action. Although many individuals have strong biospheric values, contextual barriers can inhibit their climate actions. Notably, policies and contextual changes that reduce contextual barriers can motivate and enable individuals to act on their biospheric values. In addition, public participation may better engage public values in climate policies and actions as to increase their acceptability. Finally, correcting biases that others have weaker biospheric values than oneself may also motivate individuals to support and take climate action.

What lessons do the first Nationally Determined Contribution (NDC) formulation process and implementation outcome provide to the enhanced/updated NDC? A reality check from Nepal

Nationally Determined Contributions (NDCs) are the foundation of the Paris Agreement. So far, 190 Parties have submitted their NDCs. But how the NDC can be made comprehensive, unanimous and implementable so that the Paris climate goals can be achieved has been a matter of growing concern among policymakers, academics, and practitioners. Aiming to bridge the knowledge gap about institutional deliberation of NDCs, we assessed the formulation process and implementation outcome of Nepal's first NDC by employing qualitative research methods. We undertook semi-structured interviews (n = 10) with all experts and bureaucrats engaged in the NDC formulation process. Moreover, we conducted group discussions (n = 18) with nine stakeholder groups and key informant surveys (n = 12) with four stakeholder groups representing cross-sectoral ministries, private-sector, (retired) bureaucrats, and media people. We also reviewed contemporary literature and progress report of sectoral governments and other related institutions. The collected data were then analyzed by applying the discursive institutional framework. As NDC is a national political plan of climate action and demands support and commitment from a wide spectrum of society, our results, however, revealed that Nepal's first NDC was formulated without engaging politicians and the other major state and non-state actors. Moreover, the country's NDC was framed and articulated only for fulfilling international obligation (or commitment) and getting international fund, but not as a determined national climate plan of action for expediting climate action at (sub) national level. Our analysis further found that very few institutions including policies, programmes, and budgets were arranged for translating targets of the NDC into action. Because of these shortcomings, Nepal's first NDC could not achieve most of its stipulated targets. Based on the analysis and results of our study, we have discussed and recommended some pathways that are critical for the formulation and implementation of enhanced/updated NDCs in Nepal and the other countries.

Reviving tourism industry post-COVID-19: A resilience-based framework

The COVID-19 pandemic struck the tourism industry severely. Based on the review of 35 papers that studied the tourism industry in the wake of the pandemic, we propose a resilience-based framework for reviving the global tourism industry post-COVID-19. Our framework outlines four prominent factors for building resilience in the industry: government response, technology innovation, local belongingness, and consumer and employee confidence. We argue that using such inclusive resilience; the tourism industry may transform into a new global economic order characterized by sustainable tourism, society's well-being, climate action, and the involvement of local communities. We also offer directions for future research in the area.

Energy generation from anaerobic co-digestion of food waste, cow dung and piggery dung

The study investigated bioenergy generation from anaerobic co-digestion of food wastes (FW), cow dung (CD) and piggery dung (PD). The physicochemical parameters of the substrates were determined before and after digestion following standard procedures after mechanical pretreatment. Throughout the study, pH remained slightly alkaline while temperature varied between 26 and 32 °C. The highest cumulative biogas yield of 0.0488 L was recorded from the digestion of FW + CD + PD on the ninth day. After analyses, the highest methane content of 64.6 was obtained from the digestion of FW + PD while the lowest (54.0%) was from the digestion of FW only. Overall, cumulative biogas production for the four digestion regimes followed the order: FW + CD + PD, FW + PD, FW + CD and FW only respectively. Accumulation of VFAs was recorded at a slow rate during the digestions.

Confronting, collaborating, withdrawing? A psychiatric evaluation of three strategies to promote political climate action

This qualitative study analyzes the lack of political action to address climate change using a psychiatric lens, and frames that ambivalence lies at the core of inaction. While most politicians understand that climate action is absolutely necessary, any significant action is stalled by a number of important barriers they have to overcome. Using clinical analogies from eating disorders and the scientific literature on motivational change, this paper analyzes three current strategies that push for political action. First, using force and emotions (like confronting activists) is equivalent to playing a power struggle, which risks increasing politicians' resistance to change. Second, collaborative discussions in multilateral conferences and debates risk feeding verbal manifestos without enacting behavioural change. Withdrawal from the manifestos of politicians is a third strategy to push for change discussed in this paper. However, even after bypassing manifestos, this strategy is unlikely to succeed because the benefits of greenhouse gas emissions, linked to our current social norms, seriously outweigh the benefits of climate action. Overall, all three methods present severe flaws and are not viable solutions to help politicians implement climate action. Other enhanced options are likely to be necessary.

Developing and implementing a targeted health-focused climate communications campaign in Ontario-#MakeItBetter

SETTING

Public health practitioners are called to effectively communicate with the public on climate change. The climate crisis requires swift action that starts with public awareness of climate-related health impacts and leads to public support for individual, community and systemic actions to mitigate and adapt to climate change.

INTERVENTION

This paper discusses learnings about public opinion research and communication strategies and how a health-focused climate communication campaign-#MakeItBetter-could help to increase awareness and engage new audiences, including public health partners, in conversations about climate change in order to reduce climate-related health impacts for current and future generations. The #MakeItBetter campaign was grounded in evidence-informed messaging, being sensitive to health inequities. Emerging research and pre-campaign testing suggest that framing climate change as a health issue is a promising practice.

OUTCOMES

The #MakeItBetter campaign appeals to parents/caregivers to learn more about climate-related health impacts, take protective action for children and support multi-level climate action. The campaign launch secured 89 news stories, including multicultural media coverage. Longer-term evaluation is required to determine the campaign's effectiveness in building public support for climate action.

IMPLICATIONS

An innovative approach to climate communication that draws on the intersections between behavioural and climate sciences and engages in multi-sectoral collaboration can spur both climate action and health protection, aiding public health practitioners and partners in effectively communicating the urgency for climate action. More work is needed to support communication on climate change as an inequity multiplier and promote climate action and community resilience for health equity co-benefits.

The strength of green ties: Massachusetts cranberry grower social networks and effects on climate change attitudes and action

The cranberry, a commodity of social, cultural, and economic importance to New England, is under threat due to climatic change in this region of the United States. Yet, previous research reveals that cranberry growers have mixed attitudes about the anthropogenic roots of climate change, with many being skeptical. Building on the researchers' analysis of the personal and ecological conditions that affect climate change attitudes among cranberry growers, this paper examines the effect that key actors in the growers' social networks have on those attitudes. Through statistical analysis of survey data and content analysis of two important cranberry newsletters, the paper finds that cranberry growers' perceived importance of two key cranberry growing institutions, the "sociopolitically focused" Cape Cod Cranberry Growers' Association and the "technically focused" University of Massachusetts Cranberry Station, as well as connections to other cranberry growers, is associated in nuanced ways with growers' climate change attitudes. Drawing on the sociological theory of "social capital," the paper examines how these social ties to key actors/institutions may result in greater threat perception or worry about climate change. It then considers how "green ties," if harnessed and supported by these important actors in the cranberry grower network, might significantly mitigate climate change in the future.

Using the COVID-19 economic crisis to frame climate change as a secondary issue reduces mitigation support

The COVID-19 pandemic has understandably dominated public discourse, crowding out other important issues such as climate change. Currently, if climate change enters the arena of public debate, it primarily does so in direct relation to the pandemic. In two experiments, we investigated (1) whether portraying the response to the COVID-19 threat as a "trial run" for future climate action would increase climate-change concern and mitigation support, and (2) whether portraying climate change as a concern that needs to take a "back seat" while focus lies on economic recovery would decrease climate-change concern and mitigation support. We found no support for the effectiveness of a trial-run frame in either experiment. In Experiment 1, we found that a back-seat frame reduced participants' support for mitigative action. In Experiment 2, the back-seat framing reduced both climate-change concern and mitigation support; a combined inoculation and refutation was able to offset the drop in climate concern but not the reduction in mitigation support.

Governance of risky public goods under graduated punishment

Ensuring global cooperation often poses governance problems shadowed by the tragedy of the commons, as wrong-doers enjoy the benefits set up by right-doers at no cost. Institutional punishment of wrong-doers is well-known to curtail their impetus as free-riders. However, institutions often have limited scope in imposing sanctions, more so when these are strict and potentially viewed as disproportionate. Inspired by the design principles proposed by the late Nobel Prize Elinor Ostrom, here we study the evolution and impact of a new form of institutional sanctioning, where punishment is graduated, growing with the incidence of free-riding. We develop an analytical model capable of identifying the conditions under which this design principle is conducive to the self-organization of stable institutions and cooperation. We employ evolutionary game theory in finite populations and non-linear public goods dilemmas in the presence of risk of global losses whose solution requires the self-organization of decision makers into an overall cooperative state. We show that graduated punishment is more effective in promoting widespread cooperation than conventional forms of punishment studied to date, being also less severe and thus, presumably, easier to implement. This effect is enhanced whenever the costs of its implementation are positively correlated with the severity of punishment. We frame our model within the context of the global reduction of carbon emissions, but the results are shown to be general enough to be applicable to other collective action problems, shedding further light into the origins of Human institutions.