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The purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods.

The daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities.

The noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets.

The present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions.

The current study has the following points of originality. First, to the best of the author’s knowledge, this is the first study to investigate the individual commodities’ roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data.

This study aims to reveal residents’ individual perceptions of nature-based destination development and preferences for infrastructure and tourism superstructure development among communities that rely heavily on wildlife tourism.

A discrete choice experiment (DCE) was used among the Maasai community based in the villages and towns near the Maasai Mara National Reserve in Kenya. The attributes included type of tourism accommodation, location of tourism accommodations, types of access roads (tarmac or marram), tourist numbers and desired land-use options (between tourism development, livestock grazing and agriculture). A DCE analysis with hierarchical Bayes estimation was performed.

It revealed that the introduction to land-use restrictions and the location of tourism accommodations were the most important attributes for the respondents, with average importance values of 30.36% and 24.02%, respectively. A significant less important attribute was the types of access roads with an average importance of just 8.38%. Cluster analysis revealed widespread heterogeneity in preferences.

The survey-based DCE was conducted in the Maasai Mara National Reserve, Kenya, and therefore may not be relevant in other contexts. The focus was also only on the residents’ preferences. The findings broaden the knowledge on tourism developments and residents’ support for development and management of protected areas.

For policymakers, conservation practitioners and tourism businesses, this study provides a source of reference for understanding the development preferences of the Maasai community. In general, the study contributes to a better understanding of local communities in relations to tourism development and residents’ support for developments and management of protected areas (PAs).

This study fills the gap in the literature on tourism development and residents’ support for developments in PAs by presenting some limits of acceptable and desirable use of PAs among a community that has a complex coexistence with a wildlife tourism destination. It provides an alternative perspective for future research by examining residents’ choice towards destination development and preferences for infrastructure and tourism superstructure development using an experimental approach.

This study examines the carbon emissions of Australia’s agriculture, forestry and fishing sectors from a consumption perspective to develop effective policy frameworks. The objective is to identify key supply chains, industries and products contributing to these emissions and provide recommendations for sustainable development.

A multiregional input-output lifecycle assessment was conducted using the Australian Industrial Ecology Virtual Laboratory (IELab) platform to disaggregate sectors and enable benchmarking against other economic sectors.

In 2018, the “agriculture, forestry, and fishing” sector was responsible for 12.15% of Australia’s carbon footprint. Major contributors included the “electricity, gas, water, and waste” category (26.1%) and the sector’s activities (24.3%). The “transport, postal, and warehousing” sector also contributed 18.4%. Within the industry, the agriculture subsector had the highest impact (71.3%), followed by forestry and logging (15%) and fishing, hunting and trapping (7.6%). Aquaculture and supporting services contributed 6.1%.

The principal constraint encountered by the present study pertained to the availability of up-to-date data. The latest accessible data for quantifying the carbon footprint within Australia’s agriculture, forestry and fishing sector, utilizing the Input-Output analysis methodology through the Australian Industrial Ecology Virtual Laboratory (IELab) platform, about 2018.

The findings of this study provide policymakers with detailed insights into the carbon footprints of key sectors, highlighting the contributions from each subsector. This information can be directly used to develop effective emission-reduction policies, with a focus on reducing emissions in utility services, transport and warehousing.

The study, by raising public awareness of the significant role of industrial agricultural methods in Australia’s carbon footprint and emphasizing the importance of renewable energy and sustainable fuels for electricity generation and road transport, underscores the urgent need for action to mitigate climate change.

This study stands out by not only identifying the most impactful industries but also by providing specific strategies to reduce their emissions. It offers a comprehensive breakdown of specific agricultural activities and outlines mitigation strategies for utility services, agricultural operations and transport, thereby adding a unique perspective to the existing knowledge.

Climate change negatively affects agriculture and food security, and jeopardizes Myanmar's agriculture, which is vital to ensure food security, rural livelihoods, and the economy. This study explores the asymmetric impacts of climate change on Myanmar's agricultural sector.

We utilize the nonlinear autoregressive distributed lag (NARDL) approach for the years 1991–2020, the Wald test to validate the asymmetric relationship between climate change and agriculture, and the FMOLS and DOLS approaches to confirm the validity of the outcomes.

Our findings reveal that temperature has a positive impact on Myanmar's agriculture, whereas rainfall and CO2 have negative effects over the long and short terms. Evidently, decreasing temperatures more favorably impact agriculture than increasing temperatures, while increasing rainfall more negatively impacts agriculture than decreasing rainfall. Increasing carbon emissions have a more detrimental effect on agriculture than decreasing them.

We gathered data over periods longer than 30 years to provide more robust findings. However, owing to data limitations, such as missing values or unavailability, the study period spans from 1991 to 2020.

This study contributes to the existing literature on the asymmetric effects of climatic and non-climatic factors on agriculture. It is the first study in Myanmar to use the NARDL approach to measuring the effects of climate change on both the agricultural gross production index and value, providing robust findings.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

This study aims to examine the asymmetric effects of average temperature (TP) and rainfall (RF) on the Moroccan food security, measured by the food production index (FPI), using annual data from 1961 to 2020.

The study uses the Climate Change and Food Security Framework (CCFS) developed by the Food and Agriculture Organization (FAO) and employs the nonlinear auto-regressive distributed lag (NARDL) model and various econometric techniques to show the effects of climate variability in the short and long-term. It also examines if the impacts on Moroccan food security are asymmetric by analyzing the positive and negative partial sums of mean temperature and rainfall.

The study shows that RF has a long-term relationship with FPI, with increased RF leading to increased FPI and decreased RF leading to decreased FPI. FPI responds more strongly and persistently to a positive shock in RF than to an adverse shock. The study also identifies an asymmetric relationship between FPI and RF, with increased TP enhancing food output in the long run and a decrease reducing food production in the long run.

The current study could have some limitations. For instance, there are several other non-climate factors that might potentially impact food security. In particular, CO2 emissions which from the literature is a key variable that represent climate change impact on food security, was not included. The present research has not included those factors mainly because adding more variables to the model reduces the degree of freedom available to estimate the parameters, resulting in inaccurate results.

This paper contributes to the food security literature by utilizing the latest asymmetry methodology to decompose climate changes into their positive and negative trends and examining the contrasting impacts food production.

This article examines the dairy value chain in Southern Peru and identifies four critical success factors that can enhance the local situation.

The study employed descriptive research using semi-structured interviews with entrepreneurs from 17 cheese factories across eight districts, namely Azángaro, Ayaviri, Pucara, Lampa, Cabana, Acora, Pomata and Puno. Quantitative market data were also gathered and analyzed alongside qualitative views.

The study identified four critical issues: quality concerns in milk production, suboptimal managerial practices of cheese-processing plants, lack of compliance to regulations, particularly hygiene and environmental ones, and inadequate access to finance. The findings reveal a gap between the practices of the Puno region's dairy industry and world-class standards for cheese production. Urgent actions are required to improve product quality, increase access to finance, enhance managerial education and ensure compliance with regulations.

Results suggest critical issues to be prioritized, but the article does not propose how to solve the problems identified. External factors, such as economic changes, were also not considered. Interviews were conducted exclusively with cheese processing entrepreneurs, not milk producers.

This case study provides an insight into the interior of Peru, an under-researched region facing several development challenges. The findings have significant implications for dairy value chain stakeholders in Peru and other similar contexts.

This paper aims to analyse how local agri-food systems (LAFS), compared to other production models, can offer innovative responses to the important environmental challenges facing food production under the twin transition. These responses are more conducive to community inclusion and local development.

The paper combines territorial development, clusters and industrial districts literature with studies on agri-food industry environmental problems and twin transition technologies to develop an agri-food systems typology. This typology is based on a territorial approach to environmental challenges of food production and serves to illustrate the ways in which LAFS can provide innovative responses to these challenges.

The study allows to visualise the differences between LAFS and other agri-food production models, showing how the operationalisation and implementation of digitisation occur at territorial level and how rural communities are involved in the process. The theoretical proposal emphasises not assuming that technology is inherently beneficial but ensuring that its implementation is inclusive and generates social value for the communities.

The paper aims to enrich future research by adopting a territorial perspective to study the twin transition challenges associated with food production systems.

The research aims to assess the relationship between ecological innovation, green productivity, sustainable development and agricultural productivity in Vietnam. The agricultural sector of Vietnam has been observed with new opportunities which have fostered its productivity and growth.

The study uses a range of methods where initially, the researcher used descriptive analysis, cointegration and unit root tests. Secondly, Quantile Autoregressive Distributed Lag (QARDL) is used to assess the short and long run effects. The QARDL methodology is employed to capture the relationship between variables. Through this approach, the researcher is able to examine the scale of the interaction between dependent and independent variables.

The unique findings drawn through statistical techniques are also a great addition to the context of literature related to Vietnam’s agricultural productivity. Practical insights can also not be denied as the study provides beneficial guidelines for Vietnam’s agricultural sector to refine agricultural productivity.

Scholars are advised to use strong literary techniques to overcome these limitations and give a more thorough investigation into the same ideas. The availability and dependability of data was one of the primary challenges in carrying out this study. Vietnam has made significant advances in the collection and documentation of agricultural data, but there might still be gaps in the availability of thorough and current data on ecological innovation, green production and sustainable development.

Vietnam’s unique socioeconomic, cultural and environmental features influence how ecological innovation, green productivity, sustainable development and agricultural production are interconnected. Consequently, consideration should be taken when applying the results to various scenarios.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.