Climate Change Research Research Articles

Unbounding the future: New directions for climate‐changed geographies

AbstractThis commentary reflects on the contributions that human geography has made to climate change research, as well as future directions for human geographic work on climate change. I suggest that one of the key achievements of the field is to render climate change a boundary object: an entity whose interpretive flexibility allows it to become a shared object of knowledge that, in principle, facilitates collaboration across disparate communities of practice. However, the ways in which that contribution has been made, and made possible, have effects that ripple elsewhere. While the subfield of political ecology and its historiographical methods have played vital roles in laying the intellectual foundations for human geographic work on climate change, and thus its ability to construct climate change as a boundary object in the first place, I suggest that they and other neo‐Marxian approaches to the study of the (climate‐changing) environment tend to diminish human geographic engagement with the future as an object of scholarly inquiry. To address that limitation and help push climate‐changed geographies in novel directions, I suggest that climate‐changed futures be engaged problematically. I then conclude with a discussion of key empirical domains—chiefly climate reparations and climate adaptation—where such problem‐oriented work may be productively applied.

Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

General circulation models are vital tools in climate change research, profoundly influencing numerous hydroclimatological studies, but often exhibit systematic biases due to limitations in parameterization. To correct these biases, bias correction (BC) methods, which are heavily dependent on the quality of historical reference data, are employed. Ideally, accurate data comes from a dense network of gauges, but due to their limited availability, reanalysis data is frequently used instead. Although reanalysis data provides consistent global and temporal coverage, its assimilated nature can introduce additional biases, potentially increasing error propagation particularly in the multivariate perspective. Thus, this study investigates the reliability of reanalysis data as a reference for multivariate bias correction (MBC) and utilizes a two-stage bias correction approach that integrates both gauge and reanalysis data to leverage their respective strengths. Conducting a case study in South Korea, we utilized gauge data, ERA5 reanalysis data, and outputs from 10 CMIP6 General Circulation Models. We implemented three-nested experiments to test the robustness of this two-stage approach across various dimensions. Results show that using reanalysis data as a reference can lead to bias propagation, especially misrepresenting precipitation and wind dependencies in high elevation areas. However, the two-stage approach has shown an enhancement in mitigating these limitations, even when gauge data is temporally sparse, and across different combinations of MBC methods. Finally, we highlight the current limitations and potential for future research to focus on effectively combining gauge and reanalysis data to compensate for each other’s weaknesses, thereby enhancing the accuracy and reliability in future climate realizations.

Improving the ERA5-Land Temperature Product through a Deep Spatiotemporal Model That Uses Fused Multi-Source Remote Sensing Data

Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region.

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

AbstractThe Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

Simultaneous Detection of Major Greenhouse Gases with Multiresonator Photoacoustic Spectroscopy.

Greenhouse gas (GHG) detection plays an important role in climate change research and industry applications. A novel photoacoustic spectroscopy (PAS) sensor based on multiple resonators has been developed for the detection of GHGs. The major GHGs CO2, CH4, and N2O were measured simultaneously using only one acoustic sensor by coupling three acoustic resonators into a photoacoustic cell. A sinusoidal voltage signal-driven noise source was integrated into a multiresonator photoacoustic cell, allowing convenient calibration of the resonant frequency of the photoacoustic cell. The performance of the sensor was further enhanced by reflecting a laser beam four times in the photoacoustic cell. Allan deviation analysis showed that the minimum detection limits of 2.7 ppm, 90 ppb, and 1 ppb could be achieved for CO2, CH4, and N2O, respectively, over a 300 s integration time. The feasibility of the system was confirmed by continuous measurements of the three major GHGs from different sources for up to 10 h.

Synergistic Development Pathways for National Parks and Local Regions: Shared Socioeconomic Pathway Scenario Forecasting and Optimization

National parks play a crucial role in protecting ecosystems and biodiversity while facing challenges in balancing nature conservation and economic development. However, because of the difficulty in the unified simulation of natural protection functions and regional socioeconomic development, there is a lack of integrated prediction research on the comprehensive development pathways of national parks and their surrounding areas. This study adopts the Shared Socioeconomic Pathway (SSP) framework that links climate change research with socioeconomic development paths, taking China’s first national park—Sanjiangyuan National Park—region as an example, to conduct research on the synergistic development path of regional socioeconomic development and national parks. The model design includes five typical paths to cover a wide range of socioeconomic development possibilities. These paths are based on different assumptions, including factors such as population growth, economic development, energy use, technological progress, and policy choices. By applying scenario planning methods, optimal development pathways are identified based on environmental, economic, and social priorities. The results show that GDP growth is fastest under the sustainable development and fossil fuel development pathways. By 2050, the population difference under different pathways will approach 100,000, and the GDP gap will be close to CNY 200 billion. This study provides valuable insights for the planning, decision-making, and management of Sanjiangyuan and similar national parks and their surrounding areas, contributing to the promotion of sustainable ecological and economic development of national parks and their regions.

Construction of Coordination Spaces with Narrow Pore Windows in Co-Based Metal-Organic Frameworks toward CO2/N2 Separation.

Carbon emission reduction is an important measure to mitigate the greenhouse effect, which has become a hotspot in global climate change research. To contribute to this, here, we fabricated two Co-based metal-organic frameworks (Co-MOFs), namely, {[Co3(NTB)2(bib)]·(DMA)2·(H2O)4}n (DZU-211) and {[Co3(NTB)2(bmip)]·(DMA)2}n (DZU-212) (H3NTB = 4,4',4″-nitrilotribenzoic acid, bib = 1,4-bis(imidazol-1-yl)-butane, bmip = 1,3-bis(2-methyl-1H-imidazol-1-yl)propane) to realize efficient CO2/N2 separation by dividing coordination spaces into suitable pores with narrow windows. DZU-211 reveals a 3D open porous framework, while DZU-212 exhibits a 3D double-fold interpenetrated structure. The two MOFs both possess large coordination spaces and small open pore sizes, via the bib ligand insertion and framework interpenetration, respectively. Comparatively, DZU-211 reveals superior selective CO2 uptake properties due to its more suitable pore characteristics. Gas sorption experiments show that DZU-211 has a CO2 uptake of 52.6 cm3 g-1 with a high simulated CO2/N2 selectivity of 101.7 (298 K, 1 atm) and a moderate initial adsorption heat of 38.1 kJ mol-1. Moreover, dynamic breakthrough experiments confirm the potential application of DZU-211 as a CO2 separation material from postcombustion flue gases.

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning.

Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data

Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as “observed GPP”) retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m−2 d−1) and 0.83 (1.49 gC m−2 d−1), respectively, which increased (decreased) by 0.17–0.03 (0.58–0.12 gC m−2 d−1) and 0.11–0.02 (0.39–0.10 gC m−2 d−1) compared with the R2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R2 by 0.33 and decreasing RMSE by 0.97 gC m−2 d−1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.

Patterns and Trends in Northern Hemisphere River Ice Phenology from 2000 to 2021

River ice plays a crucial role in hydrology, ecosystems, and socio-economic systems in mid-to-high latitude regions. However, there is a notable gap in the analysis of patterns and trends in river ice phenologies and their influence factors at the river level. In this study, we developed a practical method to identify river ice phenologies by leveraging Landsat observations from 2000 to 2021, and established a comprehensive dataset encompassing 7970 river segments across the entire Northern Hemisphere. Our results revealed that the mean freeze-up date, break-up date, and ice duration for these river segments were October 24, May 4, and 192.1 days, respectively. Notably, 56% of the river segments exhibited a warming trend, with the average freeze-up date delayed by 2.7 days per decade, break-up date advanced by 2.5 days per decade, and ice duration shortened by 5.5 days per decade—nearly five times the documented decrease rate over the past 150 years. Furthermore, high-latitude polar regions and the Qinghai-Tibet Plateau demonstrated the most pronounced decreases in ice duration, exceeding 7.0 days per decade on average. We observed a substantial relative contribution, reaching up to 70%, of surface air temperature (SAT) in middle and low latitude regions characterized by relatively high winter temperatures. However, as winter temperatures decrease, the relative influence of SAT diminishes, while the contributions of precipitation, solar radiation, and residuals increase. Our dataset provided essential baseline for further understanding the fundamental physical processes within river systems, offering crucial insights for river management, hydrological disaster risk control, climate change research, and ecosystem conservation efforts.

Tourism and the climate crisis

2023 was a landmark year for climate change, with hundreds of climate records broken around the world concurrent with the conclusion of the IPCC Sixth Assessment (AR6) process and the first United Nations (UN) Stocktake on climate action. This special issue builds on the AR6 with three papers on key global tourism and climate change knowledge gaps (tourism and climate policy integration, pathways to deep emission reductions, tourism demand) and nine in-depth assessments of tourism climate and carbon risk in each of the IPCC regions. These important contributions of 65 different authors from 30 countries also supported the first ever global stocktake of climate action in the tourism sector. The papers in this special issue make clear that global tourism as we know it in the early twenty first century will be transformed by the climate crisis. Based on the collective contributions, this introduction to the special issue summarizes the state of tourism and climate change research, sets out a research agenda related to the low carbon transition, adapting to accelerating climate disruption, and climate justice, and emphasizes the urgent need to mobilize the tourism academy in this decisive decade for climate action.

Development of L-band fully polarimetric SAR algorithm for forest biomass retrieval using 7SD and random forest regression

Forest biomass is essential for carbon budgeting, biodiversity health, and climate change research. This study developed a novel L-band fully polarimetric SAR method for estimating aboveground biomass (AGB) in a forested region in India. Biomass was estimated using fully polarimetric ALOS-2/PALSAR-2 data sets and field AGB measurements. To estimate forest AGB, the algorithm employs a seven-component scattering power decomposition (7SD) and a random forest regression (RFR) machine-learning approach. By implementing the 7SD model, the seven scattering powers were extracted from ALOS-2/PALSAR-2 inverted for aboveground biomass. The field data were used to validate the biomass estimated by the model. The 7SD model estimated AGB in Shivamogga was consistent with field measured biomass. The root mean squared error (RMSE) and relative RMSE with respect to mean AGB were 21.94 Mg/ha and 19.46, respectively, which are within acceptable ranges. The 7SD model was also used for cross-validation at Tundi Forest, where the relative RMSE with respect to the mean AGB was 22.9. The scattering powers generated from L-band fully polarimetric data can be useful in tropical forest AGB estimation.

What Is Sustainability? A Reflection From the Perspective of Biodiversity Informatics

In recent years, the word sustainability has been used in multiple domains ranging from finance to packaging, and the list of products and activities claiming to be sustainable grows day after day. However, a general and unambiguous definition of the term sustainability is not available and might not even be advisable because sustainability is a multifaceted concept that takes its meaning from the context of use and cannot provide a set of rules applicable everywhere (Ramsey 2015). Therefore, it remains a task for all the fields that strive to be sustainable to carve out a working definition that suits their needs. Addressing the meaning of sustainability in biodiversity informatics requires paying special attention to the data practices and infrastructures of this discipline. Over time, there has been a steep increase in the amount of biodiversity data created and shared and, at the same time, a rapid proliferation of use cases for biodiversity data. For instance, fields as different as spacial planning (Underwood et al. 2018), climate change research (Hirsch 2017), and finance (Elliot et al. 2024) rely now on biodiversity data. This growth can become unmanageable for biodiversity science, if its data practices and infrastructures do not scale up efficiently and are not sufficiently flexible to cater to the needs of a larger and more varied group of stakeholders. Biodiversity informatics has mainly addressed the financial sustainability of its infrastructures so far (Cook and Cochrane 2023; Kalfatovic et al. 2023). However, environmental and social sustainability are equally important to ensure that biodiversity science achieves its full potential. For instance, data infrastructures with lower environmental impact typically require less electricity to run continuously, and this will save money in the long run, especially at times of significant fluctuations in energy prices. Again, if biodiversity data are collected and shared while keeping in mind the necessities of multiple stakeholders, the user community will become larger and this, in turn, will offer the chance to develop fairer cost contribution agreements for running data infrastructures. More examples could be added to make the case that the three pillars of sustainability—economy, society, environment (Purvis et al. 2018)—are all equally relevant when examining data practices and infrastructures in biodiversity science. Hence, there is a need to open up a discussion on sustainability within the biodiversity community, and to promote an exchange on the topic among different stakeholders interested in biodiversity data and working with them. A community effort is required to agree upon an effective definition of sustainability in biodiversity informatics. Only such a community effort can translate this definition into practical criteria and recommendations for the sustainability of biodiversity data and infrastructures, and set milestones for their implementation. The Society for the Preservation of Natural History Collections and Biodiversity Information Standards (TDWG) are authoritative members of the biodiversity community. The aim of the talk is to involve their members in the discussion on data practices and infrastructures, addressing all the three pillars of sustainability: economy, society, and environment.

Characteristics and influencing factors of carbon source/sink variations in the Zoige grassland wetland ecological function zone on the eastern slope of the Tibetan Plateau

Net ecosystem productivity (NEP) refers to the portion of net primary productivity (NPP) that is available for carbon cycling in terrestrial ecosystems after subtracting photosynthetic carbon consumed by heterotrophic respiration. The amount of the NEP reflects the size of carbon sinks/sources in terrestrial ecosystems, holding great significance for the research of climate change and global carbon cycle. In this study, the NEP of the Zoige grassland wetland ecological function zone (ZGW) on the eastern slope of the Tibetan Plateau from 2001 to 2020 is estimated by using the improved Carnegie-Ames-Stanford Approach model for NPP and a statistical model for soil heterotrophic respiration, based on the meteorological data, vegetation data and socioeconomic data. Additionally, the spatio-temporal variations of the NEP are analyzed, and the influences of natural factors and anthropogenic activities on the NEP are investigated. The results indicate that the ZGW overall plays a role as a carbon sink, and the carbon sink area accounts for approximately 99.3% of the whole ZGW. The annual average NEP in the study area is 447.9 g·m−2, showing a gradual increase at a rate of 5.0 g·m−2·a−1, although the increasing trend is not significant. The carbon sink capacity increased in 93.5% of the ZGW, remained relatively stable in 5.9% of the ZGW, and decreased and significantly decreased in 0.6% of the ZGW. Climate warming and humidifying promote the enhancement of carbon sink capacity in the ecosystem of the ZGW, and precipitation is the dominant climatic factor influencing NEP variations. Natural factors are the determinants of NEP variations, while anthropogenic activities play a secondary role. The implementation of ecological restoration and management projects in the areas along the Yellow River, around the main roads and the core area of wetlands, as well as the continuation of green and coordinated development policies of orderly developing grassland resources, is conducive to enhancing vegetation carbon sink capacity of the ZGW.

Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

Prediction and analysis of sea surface temperature based on LSTM-transformer model

This paper introduces a novel method for predicting sea surface temperature (SST) using a hybrid LSTM-Transformer model. The study utilizes the ERA5 dataset for SST prediction at six specific locations near China. The LSTM-Transformer model combines the temporal processing capability of LSTM with the efficient data processing power of Transformer, showing superior performance in reducing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), as well as improving the R-squared (R²) value, compared to standalone LSTM, Transformer, and traditional Logistic Regression (LR) models. This is particularly evident in spring and autumn, indicating its robustness to seasonal changes. The model's performance varies across different geographic locations, with lower prediction errors in low-latitude and open sea areas, attributed to the less complex environmental dynamics compared to continental shelf areas. Overall, the LSTM-Transformer hybrid model presents a significant advancement in SST prediction, providing important implications for fisheries, meteorology, and climate change research.

Placing Islands at Risk: Developmentalism, Militarism, Geopolitics, and the Sendai Framework for Disaster Risk Reduction

AbstractIslands have come to be seen as a distinct object of disaster risk and climate change policy and research. This is reflected in the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), which specifies Small Island Developing States (SIDS) as in need of specialized policies, attention, and support. This article directs an island studies perspective toward the SFDRR, discussing obstacles to the framework’s implementation in island contexts. Focus is placed on two interrelated sets of issues: (1) problematic aspects of the concepts of “development” as it is applied to islands (particularly in the SIDS category); and (2) international cooperation, militarism, and geopolitics. The study found that although island societies can benefit from the attention brought to them by the SFDRR, the framework engages in rhetoric that may limit island possibilities and potentials while distracting from more fundamental changes that should be made by other state and non-state actors.

Knowledge exchange in the arctic environmental studies: Bridging science and the local community in dialogue

In the context of accelerated climate dynamics and uncertainties surrounding climate change research, collaborative approaches to knowledge generation are increasingly cited as a pathway needed for navigating the complexity of challenges. There is a widespread consensus that researcher-community joint expertise deepens our understanding of climatic changes. However, how this unfolds in practical settings lacks rigorous empirical support in the landscape of contemporary environmental studies. The article focuses on the intrinsic process of expert knowledge exchange and creating knowledge-in-action space for meaningful community-researcher partnerships. Addressing these issues draws upon experiences facilitating public events for diverse audiences in different countries and varying settings of scale, format and level of engagement. This account refers to the essential role of dialogue, interactivity, culturally sensitive devotion and visualization as powerful tools in building a platform for knowledge co-creation. It analyzes different ways of designing knowledge exchange that give access to a spectrum of experimentation, self-reflection and liaison with art but also requires mobilizing ethics and flexibility.

Differential responses of soil bacteria, fungi and protists to root exudates and temperature

The impact of climate warming on soil microbes has been well documented, with studies revealing its effects on diversity, community structure and network dynamics. However, the consistency of soil microbial community assembly, particularly in response to diverse plant root exudates under varying temperature conditions, remains an unresolved issue. To address this issue, we employed a growth chamber to integrate temperature and root exudates in a controlled experiment to examine the response of soil bacteria, fungi, and protists. Our findings revealed that temperature independently regulated microbial diversity, with distinct patterns observed among bacteria, fungi, and protists. Both root exudates and temperature significantly influenced microbial community composition, yet interpretations of these factors varied among prokaryotes and eukaryotes. In addition to phototrophic bacteria and protists, as well as protistan consumers, root exudates determined to varying degrees the enrichment of other microbial functional guilds at specific temperatures. The effects of temperature and root exudates on microbial co-occurrence patterns were interdependent; root exudates primarily simplified the network at low and high temperatures, while responses to temperature varied between single and mixed exudate treatments. Moreover, temperature altered the composition of keystone species within the microbial network, while root exudates led to a decrease in their number. These results emphasize the substantial impact of plant root exudates on soil microbial community responses to temperature, underscoring the necessity for future climate change research to incorporate additional environmental variables.

The Role of Philosophy in Addressing Climate Change

Purpose: The general objective of this study was to investigate the role of philosophy in addressing climate change. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to philosophy in addressing climate change. The study underscored the indispensable role of philosophy in combating climate change by examining its ethical, epistemological, and socio-political dimensions. It highlighted the importance of ethical theories like utilitarianism and intergenerational justice in ensuring climate policies are just and equitable. The study emphasized epistemic justice, advocating for the inclusion of marginalized perspectives to create more inclusive climate policies. It also revealed the interconnectedness of environmental and social justice, calling for holistic climate strategies that address social inequalities. Overall, the study demonstrated that integrating philosophical insights into climate action fosters a more ethical, effective, and sustainable response to the climate crisis. Unique Contribution to Theory, Practice and Policy: The Utilitarianism, Intergenerational Justice and Epistemic Justice Theory may be used to anchor future studies on the role of philosophy in addressing climate change. The study recommended incorporating a broader range of philosophical theories into climate change research, integrating ethical considerations systematically into climate action plans, and adopting ethically informed policies that prioritize justice, equity, and sustainability. It emphasized the need for ethical leadership, promoting epistemic justice by including marginalized communities in climate discourse, and fostering global cooperation through international ethical standards and strengthened partnerships. These recommendations aimed to create more comprehensive, inclusive, and effective responses to climate change.