Local Conditions Research Articles

Green hydrogen prospects in Peninsular Malaysia: a techno-economic analysis via Monte Carlo simulations

According to Malaysia's National Energy Transition Roadmap, hydrogen is a critical component of the country's energy transition. However, there is a scarcity of hydrogen studies for Peninsular Malaysian states, which limits discussions on green hydrogen production. This study employs a Monte Carlo model to assess the economic and technical factors influencing the success of green hydrogen in Peninsular Malaysia. The study focuses on three target years: 2023, 2030, and 2050, representing various stages of technological development and market adoption. The levelized cost of hydrogen (LCOH) of a 1-MW Proton Exchange Membrane (PEM) electrolyzer system ranges from $5.39 to $10.97 per kg in 2023, highlighting early-stage challenges and uncertainties. A 6-MW PEM electrolyzer system could achieve an LCOH of $3.50 to $4.72 per kg by 2030, indicating better prospects. Because of technological advancements and cost reductions, a 20-MW PEM electrolyzer system could achieve an LCOH of $3.12 to $3.64 per kg in 2050. The findings indicate that the northern regions of Peninsular Malaysia have consistently low LCOH values due to favorable geographical conditions. Due to minor variations in solar capacity factors, uncertainty distributions in LCOH remain stable across different regions. Some states may face increased uncertainty, emphasizing the need for additional policy support mechanisms to mitigate risks associated with green hydrogen investments. The sensitivity analysis shows that key cost drivers are shifting, with early-stage electrolyzer investments dominating in 2023 and electricity prices becoming more important in 2030 and 2050. Future research could focus on optimizing green hydrogen systems for areas with underdeveloped green hydrogen industries. This study contributes to informed discussions about green hydrogen production by emphasizing the importance of tailored strategies that consider local conditions and highlighting the role of Peninsular Malaysia in the energy transition.

Spawning fish maintains trophic synchrony across time and space beyond thermal drivers.

Increasing ocean temperature will speed up physiological rates of ectotherms. In fish, this is suggested to cause earlier spawning due to faster oocyte growth rates. Over time, this could cause spawning time to become decoupled from the timing of offspring food resources, a phenomenon referred to as trophic asynchrony. We used biological data, including body length, age, and gonad developmental stages collected from >125,000 individual Northeast Arctic cod (Gadus morhua) sampled between 59 and 73° N in 1980-2019. Combined with experimental data on oocyte growth rates, our analyses show that cod spawned progressively earlier by about a week per decade, partly due to ocean warming. It also appears that spawning time varied by more than 40 days, depending on year and spawning location. The significant plasticity in spawning time seems to be fine-tuned to the local phytoplankton spring bloom phenology. This ability to partly overcome thermal drivers and thus modulate spawning time could allow individuals to maximize fitness by closely tracking local environmental conditions important for offspring survival. Our finding highlights a new dimension for trophic match-mismatch and should be an important consideration in models used to predict phenology dynamics in a warmer climate.

Pathways and Environmental Impacts of Methane Migration: Case Studies in the Marcellus Shale, USA

Gas migration incidents, particularly stream contamination cases, have been rarely investigated and gone through the peer review process, with the exception of three sites in northeast Pennsylvania (Dimock and two Sugar Runs in Lycoming and Bradford counties, respectively) where air emission surveys, dissolved methane measurements, and structural (hydro)geologic interpretations have been used to demonstrate potential environmental impacts due to shale gas operations. In addition to reviewing previously published work from these three sites, we report and analyze unpublished new data trying to determine if a direct relationship between methane migration, stream contamination, and air emissions exists at those sites. Our analysis indicates that subsurface methane migration, stream methane contamination, and air emissions might not be all present or detectable at a faulty/leaky shale gas well. Which of these signs of contamination, if any, exist is largely controlled by the local (hydro)geologic conditions. In each case, the most likely migration pathway was from gas charged zones up well annular spaces to confined permeable formations, then laterally to a direct discharge or by vertically controlled joints to streams, water wells, and the atmosphere. The confining units act as barriers to the buoyant movement of stray gases, allowing subsurface travel of gas for 1–4 km from a leaky gas well. The knowledge we learn from these three sites can guide the future investigations of methane contamination cases in other regions.

Atmospheric teleconnections responsible for the dominant patterns of interannual variability in summer drought over northern Asia

Abstract Summer drought over northern Asia (NA) seriously threatens the local fragile ecological environment and social economy development. In this study, using the standardized precipitation evapotranspiration index (SPEI), we firstly identify the dominant modes of interannual variability in summer drought condition over NA, and then explore the atmospheric patterns responsible for the formation of the modes. The results show that the first empirical orthogonal function mode (EOF1) of summer SPEI over NA exhibits a meridional dipole pattern, which is influenced primarily by the Polar–Eurasian teleconnection (POL) and Circumglobal teleconnection (CGT) patterns. Under the influence of negative POL and positive CGT patterns, there is an anomalous anticyclone (cyclone) over northwestern Siberia (Lake Baikal to Northeast China). Such atmospheric circulations lead to meridional dipole patterns in air temperature, moisture condition, vertical motion, and cloud cover over NA, favoring decreased (increased) precipitation and increased (decreased) potential evapotranspiration over northern (southern) NA, finally contributing to the formation of EOF1. The EOF2 shows an approximate zonal dipole pattern, which is influenced by the British-Baikal Corridor (BBC) and Scandinavia teleconnection (SCA) patterns. The positive BBC and SCA patterns can lead to an anomalous anticyclone over the Ural Mountains and cyclone over the Lake Baikal. Such atmospheric circulations result in a zonal dipole pattern in precipitation and potential evapotranspiration over NA through changing the local moisture condition, air temperature, and radiation, consequently favoring the formation of EOF2. Fitting analysis indicates that the aforementioned atmospheric factors can explain 76% (55%) of the interannual variability of EOF1 (EOF2).

Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa

Drawing on a harmonized longitudinal dataset covering more than 55,000 smallholder farms in six African countries, we analyze changes in crop productivity from 2008 to 2019. Because smallholder farmers represent a significant fraction of the world's poorest people, agricultural productivity in this context matters for poverty reduction and for the broader achievement of the UN Sustainable Development Goals. Our analysis measures productivity trends for nationally representative samples of smallholder crop farmers, using detailed data on agricultural inputs and outputs which we integrate with detailed data on local weather and environmental conditions. In spite of government commitments and international efforts to strengthen African agriculture, we find no evidence that smallholder crop productivity improved over this 12-y period. Our preferred statistical specification of total factor productivity (TFP) suggests an overall decline in productivity of -3.5% per year. Various other models we test also find declining productivity in the overall sample, and none of them finds productivity growth. However, the different countries in our sample experienced varying trends, with some instances of growth in some regions. The results suggest that major challenges remain for agricultural development in sub-Saharan Africa. They complement previous analyses that relied primarily on aggregate national statistics to measure agricultural productivity, rather than detailed microdata.

Fungal diversity in sediments of the eastern tropical Pacific oxygen minimum zone revealed by metabarcoding.

Oxygen minimum zones (OMZ) represent ~8% of the ocean, with the Pacific as the largest and top expanding area. These regions influence marine ecosystems, promoting anaerobic microbial communities. Nevertheless, only a fraction of microbial diversity has been studied, with fungi being the less explored component. So, herein we analyzed fungal diversity patterns in surface and subsurface sediments along a bathymetric transect using metabarcoding of the ITS1 region in the OMZ of the Mexican Pacific off Mazatlán. We identified 353 amplicon sequence variants (ASV), within the Ascomycota, Basidiomycota, and Rozellomycota. Spatial patterns evidenced higher alpha diversity in nearshore and subsurface subsamples, probably due to temporal fluctuations in organic matter inputs. Small-scale heterogeneity characterized the community with the majority of ASV (269 ASV) occurring in a single subsample, hinting at the influence of local biogeochemical conditions. This baseline data evidenced a remarkable fungal diversity presenting high variation along a bathymetric and vertical transects.

Mapping Opium Poppy Cultivation: Socioeconomic Insights from Satellite Imagery

Over 30 million people globally consume illicit opiates. In recent decades, Afghanistan has accounted for 70–90% of the world’s illicit supply of opium. This production provides livelihoods to millions of Afghans, while also funneling hundreds of millions of dollars to insurgent groups every year, exacerbating corruption and insecurity, and impeding development. Remote sensing and field surveys are currently used in official estimates of total poppy cultivation area. These aggregate estimates are not suited to study the local socioeconomic conditions surrounding cultivation. Few avenues exist to generate comprehensive, fine-grained data under poor security conditions, without the use of costly surveys or data collection efforts. Here, we develop and test a new unsupervised approach to mapping cultivation using only freely available satellite imagery. For districts accounting for over 90% of total cultivation, our aggregate estimates track official statistics closely (correlation coefficient of 0.76 to 0.81). We combine these predictions with other grid-level data sources, finding that areas with poppy cultivation have poorer outcomes such as infant mortality and education, compared to areas with exclusively other agriculture. Surprisingly, poppy-growing areas have better healthcare accessibility. We discuss these findings, the limitations of mapping opium poppy cultivation, and associated ethical concerns.

Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga

Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. The classification process can be performed by examining external features such as fruit color, shape, and size. Classifying different types of mango fruits accurately can assist researchers in developing superior varieties and also aid farmers for cultivation purposes, sales, distribution, and selecting the right varieties for local growth and weather conditions. This research conducts the classification of mango types based on color from mango images using machine learning. The study compares three methods, namely Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), to determine the best method for classifying mango types based on their images. The dataset underwent preprocessing, where image sizes were standardized to 300 x 300 pixels, and color was changed to grayscale. The dataset was then divided into training and testing data with a ratio of 70:30. Subsequently, the dataset was processed using three methods, and their accuracy results were compared. The findings indicate that the Random Forest method yielded the highest accuracy compared to the other methods, with an accuracy rate of 96%. The accuracy of the SVM method was 95%, and the accuracy of the CNN method was 33%. From these results, it can be concluded that the Random Forest method is highly effective for classifying mango types based on their image compared to SVM and CNN methods.

Variation in fern species richness, assemblages and morphology along a tropical riparian humidity gradient: an example from Borneo

ABSTRACT Riparian vegetation experiences a little spatial distance humidity gradient, especially in the hyper-humid tropics. In Borneo, with almost no seasons and rather constant local climatic conditions, the riparian plant species show a wide range of morphological and physiological adaptations. This study assesses the adaptation strategies of ferns along a riparian humidity gradient in the submontane forest of Sarawak, Malaysia (Borneo). The species richness and cover, morphological traits, leaf area, leaf perimeter, leaf dissection, leaf elongation ratio, and stomatal density of 63 fern species were surveyed in six study areas along six stream transects. Significant trends in species richness and species cover were identified along the six transects, and changes in morphological traits such as leaf area, leaf perimeter, and leaf dissection were also discovered. With increasing distance from the streams (i.e. decreasing humidity), ferns tended to show longer leaves and a lower stomatal density. We conclude that the morphological traits of fronds change significantly within a short tropical riparian humidity gradient. This reveals variations in the energy investment and adaptation efficiency of ferns within a tropical forest microsite.

Building A Simple Weather Forecast Application using Python Django

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

Local environmental conditions structured discrete fish assemblages in Arctic lagoons

AbstractRapid changes in sea ice extent and changes in freshwater inputs from land are rapidly changing the nature of Arctic estuarine ecosystems. In the Beaufort Sea, these nearshore habitats are known for their high productivity and mix of marine resident and diadromous fishes that have great subsistence value for Indigenous communities. There is, however, a lack of information on the spatial variation among Arctic nearshore fish communities as related to environmental drivers. In summers of 2017–2019, we sampled fishes in four estuarine ecosystems to assess community composition and relate fish abundance to temperature, salinity, and wind conditions. We found fish communities were heterogeneous over larger spatial extents with rivers forming fresh estuarine plumes that supported diadromous species (e.g., broad whitefish Coregonus nasus), while lagoons with reduced freshwater input and higher salinities were associated with marine species (e.g., saffron cod Eleginus gracilis). West–East directional winds accounted for up to 66% of the community variation, indicating importance of the wind-driven balance between fresh and marine water masses. Salinity and temperature accounted for up to 54% and 37% of the variation among lagoon communities, respectively. Recent sea ice declines provide more opportunity for wind to influence oceanographic conditions and biological communities. Current subsistence practices, future commercial fishing opportunities, and on-going oil and gas activities benefit from a better understanding of current fish community distributions. This work provides important data on fish spatial distributions and community composition, providing a basis for fish community response to changing climatic conditions and anthropogenic use.

Implementation of a Far-Field Water Quality Model for the Simulation of Trace Elements in an Eastern Mediterranean Coastal Embayment Receiving High Anthropogenic Pressure

Water quality modeling is a key element for the support of environmental protection and policymaking. The aim of this work is to describe the application of a far-field water quality model for the simulation of marine pollution occurring from heavy metals (cadmium, lead, nickel, copper, and zinc). The highly stressed marine area of the Saronikos Gulf (Aegean Sea, Eastern Mediterranean) was chosen for investigation. Major pollution sources were identified, loads were estimated, and the model was parameterized to reproduce the local seawater conditions. The distribution of the pollutants between the dissolved and particulate phases was examined. The performance of the model set-up was evaluated using field concentration measurements. The described implementation succeeded in reproducing the observed levels of pollution and therefore can be used as a baseline configuration to examine the cumulative impact of future pollution sources; for example, accidental pollution events.

Spatio‐temporal shift in body size and plumage coloration is associated with the magnitude of climate change in a cosmopolitan owl

AbstractAimClimatic gradients shape geographic variation in phenotypic traits that are involved in animal thermoregulation. Therefore, increasing temperatures under global warming are expected to cause change over time in traits that show predictable spatial patterns according to environmental clines (body and appendage size, tegument coloration), as well exemplified by biogeographic rules and shown by increasing literature at a local scale. However, whether temporal shifts in phenotypic traits vary spatially according to the magnitude of the change in local climatic conditions is still unknown.LocationWorld.TaxonBarn owl species complex (Tyto alba group).MethodsBy using thousands of museum specimens collected across the globe from 1901 to 2018, we calculated within‐population change over time in wing length, bill length and melanin‐based plumage coloration and examined whether these trends differed across geographic regions, latitudes and gradients of climate changes.ResultsWe showed that populations exposed to an increasing warmer climate were subjected to a more marked decrease in body size, as gauged by wing length, and also absolute bill length that displayed a similar, although less steep, pattern of shrinking over time. In addition, phaeomelanin‐based ventral plumage colour has become paler in regions where the climate changed into warmer and drier, and darker where both temperature and precipitation increased.Main ConclusionsThese trends are generally coherent with the predictions based on Bergmann's and Gloger's rules, but not Allen's rule, and suggest that temporal shifts in body size and tegument colour depend on the magnitude of the alteration in climatic conditions, with populations living in regions where the climate has changed the most that are subjected to more marked phenotypic changes. Spatio‐temporal variation in climate has driven within‐species phenotypic clines, and it is expected to increase differences among populations according to the predicted further climate change.

Renewable energy and well-being in remote Indigenous communities of Canada: A panel analysis

Energy transitions in Indigenous, Northern and remote communities in Canada promise benefits that go beyond reliable, clean and affordable energy services. The Federal and Provincial governments have committed funding to get remote communities off diesel, acknowledging energy transitions' global and local benefits. Besides climate change mitigation, other benefits, including job creation, income generation, community ownership and local economic growth, are fundamental components of the value proposition of renewable energy projects. However, despite the promises, little evidence of the impacts of renewable energy on communities' local conditions exists. This article looks at the relationship between renewable energy projects and community well-being in Canada. We construct a panel of Indigenous communities and well-being levels using Census data and information about renewable energy projects for the period 1981–2016. The findings suggest that renewable energy is associated with higher levels of well-being. Concretely, having access to renewable energy increases overall well-being by 1 to 5 points on the 0–100 well-being scale, depending on the component of the well-being index considered in the analysis.

Glocalization: an analysis into it’s conceptual and strategic underpinnings

Glocalization is a “simultaneity—the co-presence—of both universalizing and particularizing tendencies” emphasizing the need for tempering the effects of local conditions on global pressures. While the increasing presence of best-known fast food chain restaurants worldwide such as McDonald’s and KFC is a manifestation of globalization, the shrewd and strategic adaptations of their menu to satiate the local palates and tastes by these “Glocal Enterprises” is illustrative of glocalization. Conversely, the chief point of glocalization perspective is: “even the most local ways of carrying out business operations are more and more co-constituted by economic and social trends that are most global.” Language has a huge role to play in addressing the cultural and linguistic barriers and designing a successful global talent management and language learning solution that is complemented by local support. When used as an extension of the “Think Global, Act Local” grassroots movement, glocalization also refers to market advantage of local businesses versus global companies. In the face of globalization, both the little guys and would-be entrepreneurs can emerge triumphant by tapping into their hometown markets and then scaling up. However, it is imperative that MNCs restructure the national economies to become and remain more resilient, sustainable, and inclusive in their globalization to glocalization migration odyssey. An attempt would be made in this conceptual Paper to address this issue—a challenge and an opportunity—for the key players by enumerating the stories of successful transition into glocalization by several popular Multi National Corporations in the Indian context.

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors.

Association between hydrometeorological conditions and infectious diarrhea in mainland China: a spatiotemporal modeling study

Infectious diarrhea imposes substantial disease burden in China, with local climate identified as a key risk factor. In this study, we aimed to explore the association between hydrometeorological conditions and the incidence of infectious diarrhea, and investigate the modification effect of urbanization, which remained unclear previously. Monthly data were collected from 1 January 2021 to 31 December 2022 from each city of mainland China on cases of infectious diarrhea other than cholera, dysentery, typhoid and paratyphoid (i.e. other infectious diarrhea, OID), temperature and precipitation. We used Palmer drought severity index (PDSI) to represent local hydrometeorological conditions (dry and wet). A spatiotemporal Bayesian hierarchical model combined with distributed lag nonlinear model was specified to explore the association between PDSI and the relative risk of OID. The effect size of hydrometeorological conditions at different urbanization levels were quantified through a linear interaction term. A total of 1999 947 OID cases were included. There was a N-shaped cumulative association between PDSI and OID incidence over a maximum lag of 4 months. The effect of extreme dry conditions persisted over lag 1–3 months and peaked at the 2-month lag (RR = 2.67, 95%CI: 2.44–2.91). By contrast, the relative risk of OID increased immediately under extreme wet conditions (RR = 1.97, 95%CI: 1.68–2.32 at lag 0 month). Children and the older population were particularly susceptible to extreme dry and wet conditions, respectively. Areas with lower urbanization rate, healthcare resources and economic levels, and higher population density showed a higher risk of OID related to extreme dry conditions. While the modification effects of these urbanization characteristics were limited for extreme wet conditions. There was a nonlinear and lagged association between hydrometeorological conditions and the incidence of OID in mainland China, which may be modified in diverse patterns by urbanization indicators. Our findings will provide valuable reference for better prevention and control of OID.

I-V response test of 60–150 W mono-crystalline solar panel

This work investigates the discrepancies in electrical parameters of mono-crystalline solar panels between Ago-Iwoye weather conditions and the manufacturer’s specified ideal conditions. Manufacturer’s specifications are typically based on 1,000 W/m2 global solar irradiance, AM 1.5, and 25°C operating temperature, while actual weather conditions at installation sites can vary significantly. Mono-crystalline (single-crystal) silicon solar panels of capacities 60, 80, 100, and 150 W were evaluated through current-voltage (I-V) response tests at an installation site in Ago-Iwoye, Nigeria, with solar irradiance exposure from 11 a.m. to 3 p.m. The analysis of I-V and P-V curves revealed a significant reduction in maximum power output by 28.6%, 25.9%, 28.9%, and 19.36%, respectively, compared to the manufacturer’s stated values. This deviation underscores the importance of considering local weather conditions during solar PV projects, and we recommend adding an additional 20%–30% of the total solar panel capacity during installations to account for variations in solar irradiance and operating temperatures, ensuring optimal performance and effective solar power generation in Ago-Iwoye and similar areas.

Winter-ground microhabitat use by differently coloured phenotypes affects return rate in a long-distance migratory bird.

Migratory bird populations are declining globally at alarming rates. Non-breeding site conditions affect breeding populations, but generalising non-breeding habitat conditions over large spatial regions cannot address potential fine-scale differences across landscapes or local populations. Plumage characteristics can mediate the effects of environmental conditions on individual fitness. However, whether different phenotypes use distinctive non-breeding sites, and whether they respond to non-breeding site conditions differently remains largely unknown. Stable isotopes (δ13C, δ15N, δ2H) of inert tissues are useful to infer habitat characteristics and geographic origins where those tissues were grown. We collected winter-grown feathers from pied flycatchers (Ficedula hypoleuca) on their breeding grounds over several years from males whose dorsal plumage colouration ranged continuously from brown to black and assessed their stable isotope values as proxies of local habitat conditions. Based on feather δ2H profiles we found that browner males spent their non-breeding season in drier habitats than black males. Assignment to origin analysis shows potential regional non-breeding ground separation between differently coloured males. High within-individual repeatability of both δ13C and δ15N indicate the pied flycatcher males return yearly to similar areas. Blacker males were more likely to return to the breeding grounds after dry years compared with brown males. The opposite was found in wet years. Our study demonstrates that different phenotypes are exposed to different non-breeding site conditions which can differentially affect individual survivorship. This has important ramifications for population dynamics under predicted climate change scenarios where especially brown phenotype pied flycatcher males may be under a risk of decreasing.

A Study on the Dynamic Evolution Paths of Social Risks in PPP Projects of Water Environmental Governance—From the Vulnerability Perspective

The Chinese economy is transitioning from high-speed development to high-quality development, and water environmental governance is a key factor promoting economic transformation. Due to low returns and high investment in China’s water environmental governance, the PPP (public–private-partnership) model is often adopted. However, the PPP model has historically faced challenges adapting to local conditions in China, leading to social risks in the PPP projects of water environmental governance. To reduce these risks, this paper takes a vulnerability perspective, employing the system dynamics simulation method to explore the dynamic evolution process of social risks in the PPP projects of water environmental governance. The main results show the following: (1) the external manifestations of social risks during the construction and operation periods vary significantly, exhibiting notable fluctuations; (2) during the construction period, there is a surge in social risks followed by a returns to lower than normal levels, then a gradual upward trend; during the operation period, social risks initially decrease to a lower level before gradually increasing; and (3) city class, relevant legal systems, and resource reserves emerge as critical factors influencing vulnerability and social risks. The higher the city class, the lower the project vulnerability; the soundness of the legal system for PPP projects can effectively reduce vulnerability and social risks; sufficient resource reserves can reduce social risks. Based on the above findings, this paper proposes several suggestions aiming to reduce the vulnerability and social risks in PPP projects, optimize the process of water environmental governance, and further promote the sustainable development of water environmental governance and the high-quality economy of China.