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Assessing Ecosystem Service Protection and Strategies: A Case Study of Queensland's Ecosystem Services in Response to Climate Change

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Submitted:

23 August 2024

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24 August 2024

Withdrawn:

09 October 2024

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Abstract
The ecosystem plays a crucial role in mitigating climate change and protecting the environment. This study employs GIS technology to conduct an in-depth analysis of the current status of Queensland's ecosystem and develops a Climate Change Vulnerability Index (CCVI) model based on six sub-factors. These sub-factors include the Exposure Index (Mean surface temperature and precipitation), the Sensitivity Index (Population density, Building density, and Proximity to forests and nature reserves), and the Adaptive Capacity Index (Nighttime light radiation). The findings indicate that the CCVI value is lowest in the southeastern coastal region of Queensland, suggesting that this area is less vulnerable to climate change. However, other coastal areas and the far western inland regions exhibit higher CCVI values, indicating greater climate vulnerability. These areas require attention and the implementation of appropriate measures. By deepening our understanding of exposure, sensitivity, and adaptive capacity, we can formulate effective policies and measures to promote sustainable development and mitigate the negative impacts of climate change on the region. This study holds significant practical implications for guiding ecological planning and management decisions in the area.
Keywords: 
Subject: Environmental and Earth Sciences  -   Geography

1. Introduction and Study Area

1.1. Introduction

In recent decades, the escalating severity of climate change has drawn the attention of scholars and governments worldwide. As per the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2023), the average global surface temperature experienced an increase of 1.1°C during the period of 2011-2020, relative to that of 1850-1900. The trend shows a clear indication of a further rise, affirming the undeniable reality of global warming. Climate change, with its profound impacts - ranging from altered precipitation patterns and sea-level rise to ocean acidification and an increased frequency of extreme weather events - has significantly reshaped our ecological environment and ecosystems.
Ecosystems represent the fundamental underpinning of human existence and development, as they provide the essential natural resources and capital necessary for human well-being and societal progress (Millennium, 2005). Unfortunately, the exacerbation of global climate change and the excessive exploitation and degradation of natural resources have resulted in adverse consequences for specific ecosystem service functions (Runting et al., 2017). Since the mid-20th century, human societies’ increasing demand for natural resources and profound global environmental pollution have posed unprecedented threats to the ability of ecosystems to provide critical services and support sustainable social development (Raudsepp et al., 2010). A series of studies have pointed out that environmental and socio-economic drivers can have a significant impact on ecosystem services. Environmental drivers such as climate change, changes in land use, and the spread of invasive species have been proven to intervene with ecosystem services to varying degrees. Similarly, socio-economic factors, such as societal development, technological progress, and policy measures, also display diverse degrees of impact on these services (Cord et al., 2017). Among these drivers, climate and land-use emerge as pivotal determinants shaping the future trajectory of ecosystem services (Peng et al., 2017).
The objective of sustainable ecosystem management lies in guaranteeing and maximizing the long-term delivery of diverse ecosystem services. However, these services are not isolated entities. The complexity of environmental factors such as land use and climatic variability, coupled with the varied demands of humanity, give rise to intricate interactions within ecological processes (Feng et al., 2017). For instance, the expansion of arable land might enhance agricultural output but can potentially lead to negative consequences, such as diminished carbon storage and heightened soil erosion risk. On the other hand, urbanization can enhance the quality of human habitats, although it may disrupt the balance of surface water and influence the regional climate negatively (Djanibekov et al., 2012; Xu et al., 2016). Global climate warming enhances plant respiration, which disrupts carbon sinks but increases sand fixation (Csavina et al., 2014). These studies indicate that factors such as urban development and climate change can impact a region’s ecological processes across multiple spatial scales. This often disrupts the natural balance or synergistic relationships among various ecosystem services. An enhancement in one ecosystem service could lead to an increase or decrease in other services (Shoemaker et al., 2018). By comprehensively assessing the consequences and impacts of land use and climate change, we can provide managers with the tools they need to mitigate losses in ecosystem services. This, in turn, fosters a reciprocal relationship between our society well-being and ecosystem services (Xu et al., 2018). The integration of scenario simulations and ecological models (such as InVEST, ARIES, SoLVES, etc.) can provide technical support for studying future ecosystem services and their trade-offs (Dong et al., 2018). In recent years, policymakers and researchers have proposed various scenarios based on future land use and climate change to improve urban development patterns (Dong et al., 2018; Chen et al., 2020).

1.2. Study Area -- Queensland

Queensland, situated in the northeastern part of Australia, is renowned for its vast and diverse ecosystems. The state boasts expansive tropical rainforests, coral reefs, beaches, and grasslands. Its tropical rainforest regions, such as Daintree Rainforest, Cunningham’s Gap, and Gold Coast, are among the world’s oldest forests and harbor a wide range of plant and animal species. These forests also provide crucial ecosystem services, such as carbon storage, water regulation, and habitat provision (Wallace & McJannet, 2012). Stretching along the Queensland coast, the Great Barrier Reef, the world’s largest coral reef system, showcases unique coral and marine biodiversity, including vibrant fish, turtles, sharks, and seaweed. The coastal wetlands of Queensland, encompassing mudflats, estuaries, and mangroves, serve as crucial habitats and breeding grounds for numerous migratory birds and aquatic species (Grech et al., 2018).
The inland areas consist of grasslands and arid zones that support resilient plants and animals adapted to dry conditions, such as kangaroos, wallabies, and emus. In the southeastern mountainous regions, like Lamington and the Great Dividing Ranges, diverse rainforests, waterfalls, and mountain lakes attract mountain climbers and nature enthusiasts. Queensland is also renowned for its captivating beaches and coastlines, including the Gold Coast and the Sunshine Coast, which draw tourists seeking beach leisure, surfing, swimming, and observing marine life.
The northern and eastern coastal regions of Queensland enjoy a tropical wet climate with hot and humid summers, pleasant and warm winters, and abundant rainfall. In contrast, the western inland areas have a semi-arid and arid climate characterized by scorching and dry summers, cool winters, and limited and unevenly distributed rainfall. The southern part of the state exhibits a temperate climate with well-defined seasons, warm summers, cool winters, and consistent rainfall throughout the year. The mountainous regions of Queensland even receive winter snowfall (Australian Bureau of Meteorology, 2023).
Queensland’s diverse climates are significantly influenced by climate change, with more frequent and severe weather events, increased temperatures, and changes in rainfall patterns. The rich and diverse ecosystems of Queensland, which are integral to the state’s economy, are under increasing threat from these climatic changes. Climate change impacts can disrupt ecosystem functioning, alter biodiversity, and thus affect the provision of essential ecosystem services (King et al., 2017; Hao & Yu, 2018).
The objective of this paper is to assess the ecosystem services in Queensland and explore the relationship between climate risks and strategies for ecosystem service management. Through this assessment, we aim to provide insights into how climate risks may impact the provision and functioning of ecosystem services, and what strategies can be implemented to protect and sustain these services under future climatic scenarios.
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Figure 1. Location map of Queensland.
Figure 1. Location map of Queensland.
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2. Literature Review and Research Questions

2.1. Literature Review

As human activities increase, their negative impacts on ecosystems expand, making the degradation of the ecological environment an urgent issue that humanity needs to address. ‘Ecosystem services,’ as manifestations of human utilization of ecosystem functions, have attracted widespread attention from scholars and managers worldwide. They have become a frontier and hot topic in disciplines such as geography, ecology, and environmental economics.
Various ecosystem service strategies have been enacted and proposed in Queensland, with a primary focus on safeguarding its rich biodiversity and unique ecosystems. These measures have been outlined in several government strategies and plans. For instance, the Queensland Climate Adaptation Strategy (Department of Environment Heritage Protection, 2017) aims to confront the impacts of climate change across numerous sectors, such as natural ecosystems, human health, emergency management, and agriculture. Additionally, it seeks to diminish greenhouse gas emissions, fortify adaptation capacity, and stimulate sustainable development.
Parallel to this, a series of strategies have emphasized coastal area management to mitigate erosion, protect habitats, and maintain water quality (Warnken & Mosadeghi, 2018). A noteworthy initiative is the Reef 2050 Long-Term Sustainability Plan, which is a collective endeavor aimed at preserving the Great Barrier Reef, with an emphasis on water quality improvement, ecosystem resilience, and community involvement (De’ath et al., 2012; Government of Queensland, 2018).
Over the past few years, there has been some advancement in Queensland’s policy framework for ecosystem services. However, the extent and speed of policy execution have been subject to debate. Critics argue that certain policies are implemented too slowly, and the range and intensity of the measures may not be sufficient to counteract existing threats effectively (Kroon et al., 2016). This suggests that we need a broader, more proactive, and comprehensive approach to protect and sustainably use ecosystem services.
The way Queensland’s climate change strategy is currently implemented heavily relies on local governments and the private sector. While this decentralization allows for customized solutions to suit unique circumstances, it also prompts concerns about regional disparities in adaptation. Previous research has indicated that sparsely populated, non-coastal regions might be more susceptible to climate crises. This vulnerability could be attributed to a combination of local resource scarcities, absence of specialized measures, or the magnitude of the climate change effects (Andrew et al., 2013; Warnken & Mosadeghi, 2018).
To effectively tackle these challenges, an approach for policy formulation and execution that is more nuanced, grounded in evidence, and region-specific is essential. Spatial analysis techniques offer a promising avenue in the investigation of ecosystem services and their balances. Spatial analysis can map the relationships among different ecosystem services in a specific region, using tools such as ecosystem service cluster analysis and overlay analysis, aiding in the effective identification of spatial variations in the trade-offs between these services.
Ecosystem service cluster analysis employs clustering and spatial auto-correlation analysis methods such as K-means clustering analysis and principal component analysis (Queiroz et al., 2015). These techniques categorize multiple ecosystem services into clusters based on their temporal or spatial characteristics. Not only does this analysis avoid redundant calculations when exploring correlations between different ecosystem services, but it also enables association analysis between land use and multiple ecosystem services. For example, Raudsepp et al. (2010) used clustering analysis to group 12 ecosystem services into six clusters, identifying trade-off types and distribution areas between provisioning services and regulating and cultural services. Turner et al. (2014) categorized 11 ecosystem services in Denmark into six ecosystem service clusters using a similar method, ultimately discerning the trade-offs between cultural, regulating, and provisioning services.
Overlay analysis employs Geographic Information Systems (GIS) tools to overlay multiple types of ecosystem service data, creating a comprehensive map of ecosystem service conditions. This approach can identify spatial variations in the supply of ecosystem services. Eigenbrod et al. (2010) used land cover types as proxies for ecosystem services such as biodiversity, recreation, and carbon storage in the UK. Through overlay analysis, they identified “hotspot” areas of ecosystem services.
The driving factors of ecosystem services include climate, land use, topography, soil, socioeconomic factors, etc. (Pan et al., 1999; Mayor et al., 2017). Among them, climate change is a key factor currently affecting the geographic evolution of ecosystems and their ability to provide ecosystem services (Mooney et al., 2009). Relevant studies mainly focus on natural landscapes such as mountains, rivers, and lakes (Zilio et al., 2017).
The Climate Change Vulnerability Index (CCVI) is used to assess and measure the vulnerability of species, regions or countries to climate change risks and uncertainties (Edmonds et al., 2020; Still et al., 2015). The CCVI index considers multiple key factors, including climate conditions, ecosystem health, socioeconomic conditions, and adaptive capacity. The index takes into account several key factors, including climatic conditions, ecosystem health, socioeconomic conditions and adaptive capacity. Computing the index requires collecting and analyzing data from a variety of sources to quantify the climate change risks faced by different regions. However, there are certain differences in objective natural conditions in different regions, so there is no absolute formula for the index. In some application cases, the index also considers multiple socioeconomic indicators of the country or region, such as poverty levels, education levels, and infrastructure, and indicators of adaptive capacity, such as government policy and planning measures (Young et al., 2015). The application of the Climate Change Vulnerability Index provides decision-makers and planners with targeted information and data, helping them identify areas or communities with high vulnerability and develop appropriate response strategies.
While overlay analysis and the CCVI have often been used independently, the potential to combine these tools offers a promising direction for understanding the spatial variations of ecosystem services and their vulnerability to climate change. Overlay analysis can map the distribution and “hotspots” of ecosystem services, and CCVI can identify regions or communities at high risk due to climate change. By combining these methods, researchers and policymakers can not only identify regions of high ecosystem service value but also understand the vulnerabilities of these services to climate changes. This integrated view could highlight the regions where interventions are needed most and inform more effective adaptation strategies. Regrettably, there is a scarcity of scholarly literature on these aspects in Australia.
While significant advancements have been made in the study of ecosystem services, including notable developments in theoretical research and modeling techniques, numerous unresolved issues remain. Existing research mainly focuses on the impact of environment or socioeconomic factors on ecosystem services. However, exploring the spatial heterogeneity of these influencing factors, determining their relative importance, and how to incorporate them into future land use planning and ecosystem management, all face ongoing challenges. Therefore, this study aims to delve into potential methods to address these challenging issues.

2.2. Research Questions

As previously mentioned, Queensland Climate Action Plan and Queensland Climate Change Adaptation Strategy are two important strategic documents developed by the Queensland government to address climate change. These strategic documents emphasize the importance of collaboration, information sharing, and monitoring and evaluation by the government with stakeholders and communities to ensure effective implementation of the strategies and achievement of the goals. Through the implementation of these strategies, Queensland aims to establish a low-carbon economy, sustainable development, and a climate-resilient region to safeguard the interests of its people, economy, and environment.
However, in the rapidly changing global climate scenario, Queensland’s ecosystem services strategy could be prone to certain inadequacies. As predicting and addressing climate change impacts inherently possess uncertainty, the current tactics may fall short in thoroughly evaluating and predicting forthcoming changes in ecosystems. This could potentially undermine the efficiency of protective and management measures. Additionally, the strategy may inadequately account for improving the resilience of ecosystems under the stress of climate change. This might result in ecosystems with low resistance to climate change, thereby making them susceptible to long-term damage. In the absence of extensive utilization of scientific information and predictive data on climate change, policymakers may find it challenging to assess and forecast the long-term impacts of climate change on ecosystem services comprehensively.
In the face of accelerated urban development in Queensland in recent years, the ecological environment is grappling with significant challenges. In such a scenario, grasping the impact of climate change on ecosystem services and balancing them for the optimal provision of comprehensive ecosystem services becomes imperative. Simultaneously, it is important to consider the capacity of ecosystem services to cater to the needs of Queensland’s residents.
Addressing the above issues, this paper raises the following scientific questions:
(1)
What are the spatial distribution characteristics of the Climate Change Vulnerability Index in Queensland? By studying the distribution of this index in different regions of Queensland, we can understand which areas are more susceptible to the impacts of climate change, thereby formulating targeted response measures accordingly.
(2)
Among the factors influencing the Climate Change Vulnerability Index in Queensland, which factors have the most significant effects? By analyzing and evaluating the contributions of different factors to the vulnerability index, we can determine which factors have a significant impact on ecosystem service provision and community vulnerability in Queensland. This information can guide the formulation of relevant policies and measures.
(3)
What strategies should be developed to improve the existing climate change adaptation strategies in different regions of Queensland? Due to the vast territory of Queensland, different regions face varying climate change risks and ecological characteristics. Therefore, specific strategies should be formulated for different regions to further improve the existing climate change adaptation strategies. These strategies may include enhancing ecosystem resilience, strengthening natural resource management, promoting sustainable development, and establishing flexible disaster risk management systems.

3. Methodology

The method of this study mainly deals with data processing and geospatial visualization.
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Figure 2. Data Processing Workflow.
Figure 2. Data Processing Workflow.
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3.1. Data Source

In this study, multiple dimensions of data indicators were used to assess the vulnerability of regions or communities to climate change, based on considerations of the Climate Change Vulnerability Index (CCVI). These indicators include mean surface temperature, mean precipitation, population, buildings, forests and nature reserves, and nighttime light radiation intensity.
Mean surface temperature and precipitation provide a baseline for regional climate, aiding in the assessment of risks and potential threats faced by different regions. It should be noted that this study uses the MODIS surface temperature product instead of the temperature dataset from meteorological stations. This is mainly due to the uneven spatial coverage of meteorological stations and the significant impact of surface temperature variations on ecosystem services (Haynes et al., 2018). Some research pointed out that variations in surface temperature will more directly affect soil moisture and biological activity, thereby affecting ecosystem services such as agricultural production and soil carbon sequestration (Jin & Dickinson, 2010). The mean precipitation data comes from the standardized data of 2287 meteorological stations from the Australian Bureau of Meteorology, of which 523 are located in the Queensland region.
Population and building density reflect the level of population concentration and urbanization in an area, with higher population density and building density potentially increasing vulnerability (Baró et al., 2016). At the same time, the presence or absence of forests and nature reserves, as well as the distance from a region to these areas, reflect the health of the local ecosystem. Nighttime light radiation intensity, as one of the indicators of economic development, can indirectly reflect the level of infrastructure and adaptive capacity in a region (Zhou et al., 2018). By analyzing these data, the climate vulnerability level of a region can be comprehensively evaluated. The specific data sources used in this study are listed in Table 1.

3.2. Calculation of Climate Change Vulnerability Index (CCVI)

The calculation of CCVI (Climate Change Vulnerability Index) involves three key factors: the degree of climate exposure of the system (climate exposure), the sensitivity to climate change (sensitivity), and the capacity to adapt to climate change (adaptive capacity) (Young et al., 2015).
Vulnerability = Climate Exposure + Sensitivity - Adaptive Capacity
In this study, the climate exposure indicators use annual mean surface temperature and precipitation as reference standards. Sensitivity represents the ability of ecosystems to climate exposure, including population density, building density, and proximity to forests and nature reserves. By combining climate exposure and sensitivity, we can describe the impact of climate change on the ecosystem. Adaptive capacity indicates the ability of the system to reduce the impacts of climate change, and in this study, nighttime light radiation intensity is used as an indicator of the level of economic development. Positive indicators are those that, as they increase, lead to an increase in CCVI. Negative indicators are those that, as they increase, result in a decrease in CCVI. Due to different units of measurement for different indicators, it is necessary to normalize each indicator.
For positive indicators, I ′ = I − I { m i n } I { m a x } − I { m i n }
For negative indicators, I ′ = I { m a x } − I I { m a x } − I { m i n }
where I ′ is the normalized value of the indicator, I is the original value of the indicator, and I { m a x } and I { m i n } represent the maximum and minimum values of the indicator, respectively.
The specific indicators used in the model of this study are presented in Table 2.
Exposure = (I1+I2)/2
Sensitivity = (I3+I4+I5)/3
Adaptive Capacity =I6

3.3. Spatial Distribution Characteristics of CCVI

The specific steps are as follows:
  • Data Acquisition and Indicator Calculation: Firstly, relevant data is collected, and specific values for each secondary indicator are calculated. For temperature data, a unit conversion is performed to convert Kelvin temperature to Celsius for further analysis. For rainfall data, the original data in CSV format containing latitude, longitude, and mean rainfall is imported into Arcgis pro software. Interpolation using the Kriging method is applied to generate a rainfall grid. Population data is processed using the Kernel Density Estimation tool to obtain information on spatial distribution. Similarly, the Kernel Density Estimation tool is utilized for building data to calculate building density grid. The proximity to forests and nature reserves is analyzed using the Euclidean distance tool to determine the adjacency of each location to these areas. Nighttime light data is clipped using the ‘Extract by Mask’ tool to retain only the data pertinent to the study area. These steps of data processing and calculation result in raster data for each secondary indicator, forming the basis for subsequent analysis.
  • Grid Division: The study area, Queensland, is divided into a series of 50km x 50km grid cells using the “Create Fishnet” function in Arcgis pro software. This grid division allows for the subdivision of the study area into independent evaluation units, facilitating data analysis and statistics.
  • Data Statistics: The “Zonal Statistics” tool is employed to calculate statistics for each secondary indicator within each grid cell. By overlaying the raster files of each secondary indicator with the grid cells, the values of each indicator within each grid cell are computed. For instance, the population density indicator is statistically analyzed by calculating the average pixel value of population density within each grid cell.
  • Index Calculation: Based on the research methodology and formulas outlined in Section 3.2, the obtained values of the secondary indicators are used to calculate the exposure index, sensitivity index, and adaptive capacity index for each grid cell. Consequently, the CCVI value is determined for each evaluation unit.
  • Result Visualization: Utilize Arcgis pro software to present the calculated index results in the form of maps, and create tables and bar charts of regional distribution in Excel. The visual representation allows decision-makers and researchers to gain a more intuitive understanding and utilize the assessment results effectively.

3.4. Analysis of Factors Influencing CCVI

The CCVI values vary significantly among different regions, and the reasons behind high CCVI values also differ from one another. In some regions, high CCVI values were attributable to higher exposure levels, indicating higher exposure to risk factors. But in other regions, higher CCVI values may be due to increased sensitivity, indicating higher sensitivity or vulnerability to risk factors. In addition, some areas with higher CCVI values may have poor adaptive capacity, implying weaker ability to deal with risks and disasters (Young et al., 2015).
To further investigate these factors, this study employed cluster analysis to examine the CCVI components, namely exposure, sensitivity, and adaptive capacity, separately. Cluster analysis helps to reveal the underlying patterns and characteristics of each component in different regions. By identifying distinct clusters, it is possible to better understand the unique characteristics of different regions with respect to CCVI, and deepening our understanding of its impact.
Cluster and outlier analysis normally employ the Anselin Local Moran’s I statistic, which identifies statistically significant “hotspots”, “coldspots”, and spatial outliers when given a set of weighted factors. Anselin Local Moran’s I statistic is a spatially auto-correlated local index based on spatial adjacency, measuring the similarity or dissimilarity between specific geographic units and their neighboring units (Anselin, 1995; Mathur, 2015). This statistic combines the attribute values of each geographic unit with those of the units’ neighbors, yielding an indicator value that describes local spatial auto-correlation.
The Anselin Local Moran’s I statistic can be represented by the following formula:
I i = n S 0 ⋅ ( x i − X ¯ ) S 2 ⋅ ∑ j w i j ⋅ ( x j − X ¯ )
where:
n is the total number of spatial units
S 0 is the sum of all spatial weights
x i is the attribute value for the ith geographic unit
X ¯ is the mean of the attribute values for all geographic units
w i j is the spatial weight between the ith and jth geographic unit
x j is the attribute value for the jth geographic unit
S 2 is the variance of the attribute values for all geographic units
The summation ∑ j is over all j spatial units
The calculation of this statistic involves defining spatial adjacency, computing a weight matrix, calculating the local Moran’s I statistic as per the formula, determining p-values and significance levels, and conducting spatial cluster analysis.

4. Results and Discussion

4.1. Results

4.1.1. Exposure Index

The data presented in Figure 3, Figure 4 and Figure 5 reveal that surface temperatures are generally lower in Queensland’s coastal and southeastern regions compared to the inland and northwestern areas. Similarly, the distribution of rainfall appears to be higher in the eastern parts and diminishes as we move westward. From the results of the Exposure Index calculation, we find that the eastern, northern coastal areas and the western inland sections of Queensland are particularly susceptible to the effects of climate change.
These findings underscore the heightened vulnerability of ecosystems in coastal Queensland and inland western Queensland to climate change-associated risks. Events such as heatwaves and droughts are likely to occur more frequently in these regions, thereby escalating the potential for damaging impacts on the ecosystem.
Contrastingly, the lower Exposure Index in the southeast suggests that the climate in this region is relatively favorable, with less impact from adverse effects of climate change. Moderate temperature and rainfall provide a more pleasant and favorable climate, reducing potential risks associated with extreme weather events. These findings highlight the different levels of exposure to climate change across different regions in Queensland.

4.1.2. Sensitivity Index

Figure 6, Figure 7, Figure 8 and Figure 9 present the analytical findings, illustrating that Queensland’s coastal regions have a significantly higher population density and a larger number of buildings compared to inland areas. This pattern reflects an intense level of urbanization along the coastline. Moreover, these coastal regions are generally closer to nature reserves. In contrast, there are fewer forests and nature reserves inland, implying a lower vegetation coverage.
Densely populated areas with many buildings usually denote a higher reliance on limited resources and infrastructure, such as food, water, housing, and healthcare facilities. This scenario could lead to issues like inefficient land use, traffic congestion, inadequate drainage systems, and a heightened risk during natural disasters and emergencies (Baró et al., 2016).
Forests and nature reserves, on the other hand, typically house a rich array of species. However, the ecological interactions within these areas tend to adapt slowly to swift climate changes, making them highly susceptible to climate change.
As Figure 9 illustrates, coastal areas with abundant forest resources and higher population and building densities exhibit a stronger ecological sensitivity. This observation suggests that coastal ecosystems are more vulnerable to external damage and climate change. In contrast, inland areas tend to be less ecologically sensitive due to their lower population densities and greater distances from forests.
Consequently, in evaluating the sensitivity of Queensland’s distinct regions, it is crucial to consider the amplifying effect of rising population and building density on climate change impacts and natural disasters. Moreover, the scarcity of forests and nature reserves underscores their vulnerability as ecosystem components. Addressing these challenges necessitates strategic measures to promote sustainable resource use, bolster infrastructure resilience, and enhance the preservation and management of nature reserves to sustain biodiversity and ecosystem health.

4.1.3. Sensitivity Index

Based on the results shown in Figure 11, we can observe that the coastal areas of Queensland have higher nighttime light brightness compared to the inland regions. The difference can be seen as a symptom of the relatively higher degree of economic development in coastal areas compared to inland regions. Nighttime light brightness could be used as a remote sensing indicator of economic development, as higher brightness is usually associated with more economic activity and infrastructure development (Zhou et al., 2018).
A more developed economic situation in coastal areas can provide more resources and capabilities to deal with shocks and challenges in disaster events. This may be reflected in stronger infrastructure, higher levels of societal adaptability, and more efficient disaster response and recovery mechanisms. For example, coastal areas may have developed transportation networks, hydroelectric systems, and more medical and emergency resources. These factors combined enable the coastal areas to effectively respond to the impacts of natural disasters and recover more quickly (Su et al., 2022).
In contrast, the inland Outback regions have sparse population, and only a few grid cells show higher nighttime light brightness. This indicates limited economic activities and infrastructure development in that area. Due to the lower population and resources in the inland regions, their economic resilience may be relatively lower, and they may face greater challenges in dealing with disaster events.
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Figure 10. Nighttime Light Radiation Intensity in Queensland.
Figure 10. Nighttime Light Radiation Intensity in Queensland.
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Figure 11. Adaptive Capacity Index and Its Secondary Indicator Visualization.
Figure 11. Adaptive Capacity Index and Its Secondary Indicator Visualization.
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4.1.4. CCVI

Figure 12 and Figure 13 indicates that, in Queensland, apart from the southeastern coastal region, the remaining coastal areas and the far western inland region have a higher CCVI. This means that these areas are more susceptible to the negative impacts of climate change.
When it comes to coastal areas, a high CCVI suggests they are facing the threat of sea-level rise. With global warming leading to glacier melt and ocean expansion, coastal areas may experience coastal erosion, shoreline retreat, and frequent storm surges, among other issues. Due to the lower elevation of these regions, they are more vulnerable to flooding and storms, which can result in damage to the population and infrastructure. In addition, climate change also affects marine ecosystems, such as coral bleaching and ocean acidification caused by rising sea temperatures and increased carbon dioxide levels, posing challenges to the sustainability of fisheries and coastal ecosystems.
For inland western Queensland, a higher CCVI indicates that they may facing challenges such as drought, water scarcity and soil degradation. These regions are typically characterized by arid, semi-desert and steppe climates, and climate change is likely to exacerbate existing drought conditions. The reduction of water resources can have serious impacts on agricultural production, ecosystems and the sustainable development of local communities. Most importantly, drought can lead to soil degradation and desertification, limiting the agricultural use of land and the stability of ecosystems.
According to the bar chart in Figure 14, when analyzed by administrative region, the Southeast coastal region has the lowest CCVI, indicating that it might be relatively less affected by the negative impacts of climate change. A lower CCVI value suggests that this area may be more resilient in the face of climate change, being better able to adapt and respond to related challenges. However, in the Northeast coastal region around Cairns, the average CCVI is the highest. This suggests that this region may be more vulnerable and more susceptible to adverse impacts of climate change.

4.2. Discussion

The top five administrative regions with the highest CCVI (Climate Change Vulnerability Index) are Cairns, Townsville, Queensland - Outback, Mackay - Isaac - Whitsunday, and Central Queensland. Except for Queensland - Outback, the other four regions are located in coastal areas.
According to the results shown in Figure 15, Figure 16 and Figure 17, the Queensland - Outback region has a higher CCVI primarily due to its higher Exposure Index and lower Adaptive Capacity Index. This means that the region not only faces higher climate change exposure but also has relatively weaker capabilities to cope with disasters. The region may be confronted with multiple risks from climate change, but its ability to respond to these risks is limited.
Apart from Queensland - Outback, the common characteristics of the other four administrative regions are high Exposure Index and Sensitivity Index. This indicates that these regions not only face high climate change exposure but are also more sensitive to the impacts of climate change. This is because they are located in coastal areas and face climate change risks associated with sea-level rise, coastal erosion, and other coastal-related factors, with higher population and infrastructure density compared to inland areas.
These four regions have a lower level of economic development compared to the southeast coastal region, resulting in relatively limited disaster response capabilities.
For the Queensland - Outback region, improvement measures to reduce the CCVI value include: Firstly, there is a need to enhance community participation and disaster management capacity through training programs to increase residents’ disaster awareness and emergency response capabilities. Secondly, improving infrastructure, including more efficient water and power supply systems, enhancing communication network coverage, improving transportation capacity to enhance disaster response capabilities. Additionally, the development and implementation of sustainable development policies can promote economic diversification and increased employment opportunities, enhancing community economic resilience.
For the other four coastal regions, improvement measures to reduce the CCVI value include: Firstly, there is a need to enhance disaster risk management capacity, including establishing effective early warning systems, developing emergency evacuation plans, and emergency response mechanisms. Secondly, strengthening the resilience of buildings and infrastructure through disaster-resistant design and construction measures to ensure their ability to withstand events like storms and floods. Additionally, enhancing community preparedness capacity, such as conducting disaster response drills, providing emergency supplies and resources, and fostering cooperation and mutual assistance among communities. Simultaneously, reducing carbon emissions and dependence on fossil fuels, and promoting the use of clean energy, such as solar and wind energy, to minimize the negative impact on climate change.

5. Limitations and Future Prospects

The limitations of this study are as follows:
Firstly, some of the data used are not the most up-to-date, such as population density. Using updated data would provide more accurate and comprehensive information, thereby enhancing the reliability and feasibility of the research. For population-related data, such as population density, it is important to obtain the latest census data or relevant statistical data to reflect the current population distribution (Mertler & Vannatta, 2010).
Secondly, the study considers relatively few sub-factors, and the model is relatively simple. The CCVI index has not been widely used in Australia for vulnerability assessments, mainly due to the fact that many of its indicators (such as exposure index) are primarily aimed at ecosystems in North America, which may limit its applicability to other regions (Tuberville et al., 2015; Young et al., 2015). However, the index provides a very comprehensive set of evaluation criteria, making it valuable for other regions to consider when assessing vulnerability of ecosystem.
To make the model more comprehensive and robust, future research could consider incorporating more factors and variables. For instance, additional climate indicators (e.g., the frequency of extreme weather events, wind forces), socioeconomic indicators (e.g., employment rate, education level, income level), infrastructure conditions (e.g., transportation, communication, healthcare infrastructure coverage and quality), and natural resource utilization (e.g., land use, water resource management) could be taken into account.
In addition, machine learning techniques could potentially contribute to the improvement of future research combining ecosystem services and climate change. The latest advancements in machine learning and data science have introduced new tools for handling high-dimensional and complex datasets, which can be applied to climate change vulnerability assessments. In future research, we can attempt to integrate techniques such as deep learning or XGBoost to capture the nonlinear relationships and complex interactions among different vulnerability factors (Lecun et. al., 2015; Chen & Guestrin, 2016).
Thirdly, due to the complexity of normalizing historical data and the uncertainties of climate predictions, this study lacks a time-series analysis. While the analysis has been conducted in the spatial dimension, the study does not consider the change of CCVI across multiple time periods, to some extent, integrating time-series analysis would better understand the dynamic evolution of climate change vulnerability. Under appropriate conditions, improved historical data and future prediction analyses could help identify trends, cyclical patterns, and the impact of extreme events on vulnerability. For example, machine learning algorithms such as Long Short-Term Memory (LSTM) networks have been proven effective for time-series analysis in climate research (Zhang et al., 2019). Such analysis could provide more comprehensive and dynamic information, which is of great value for formulating long-term adaptation measures and decisions.
Lastly, the study could further integrate scenario simulations and ecological models. For example, integrating models like InVEST, ARIES, and SoLVES can enhance the assessment of future ecosystem services and trade-offs. These models can simulate climate change vulnerability under different scenarios and quantify the effects of different strategies and interventions on vulnerability (Bagstad et al., 2013). Through scenario simulations, we can anticipate the outcomes of different decision paths and provide policymakers with more comprehensive and scientifically grounded decision support.

Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.
I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.

Acknowledgments

Throughout the writing process of this research project, I have received support from various aspects, for which I would like to express my gratitude. First and foremost, I would like to give my special thanks to my supervisor, Dr. Scott N. Lieske, for his support and guidance on this research. nI would also like to thank Dr. David Pullar for providing me with the idea for the project topic. Lastly, I am grateful for the wonderful learning experience provided by the University of Queensland over the past two years, and to all the lecturers and tutors who have greatly helped me in my study of GIS knowledge.

List of Abbreviations used in the thesis

ARIES Artificial Intelligence for Ecosystem Services
CCVI Climate Change Vulnerability Index
GIS Geographic Information System
InVEST Integrated Valuation of Environmental Services and Tradeoffs
LSTM Long Short-Term Memory
MODIS Moderate Resolution Imaging Spectroradiometer
SoLVES Social Values for Ecosystem Services

Appendix A

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Figure 3. Mean Annual Surface Temperature Distribution in Queensland.
Figure 3. Mean Annual Surface Temperature Distribution in Queensland.
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Figure 4. Mean Annual Precipitation Distribution in Queensland.
Figure 4. Mean Annual Precipitation Distribution in Queensland.
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Figure 5. Exposure Index and Its Secondary Indicators Visualization.
Figure 5. Exposure Index and Its Secondary Indicators Visualization.
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Figure 6. Population Density Distribution in Queensland.
Figure 6. Population Density Distribution in Queensland.
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Figure 7. Building Density Distribution in Queensland.
Figure 7. Building Density Distribution in Queensland.
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Figure 8. Distribution of Proximity to Forests and Nature Reserves in Queensland.
Figure 8. Distribution of Proximity to Forests and Nature Reserves in Queensland.
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Figure 9. Sensitivity Index and Its Secondary Indicators Visualization.
Figure 9. Sensitivity Index and Its Secondary Indicators Visualization.
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Figure 12. Distribution Grid of the CCVI Index in Queensland.
Figure 12. Distribution Grid of the CCVI Index in Queensland.
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Figure 13. CCVI for Each Administrative District (It uses a gradient color scheme ranging from green to red. Green represents low CCVI values, while red represents high CCVI values).
Figure 13. CCVI for Each Administrative District (It uses a gradient color scheme ranging from green to red. Green represents low CCVI values, while red represents high CCVI values).
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Figure 14. Mean CCVI for Different Administrative Regions in Queensland.
Figure 14. Mean CCVI for Different Administrative Regions in Queensland.
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Figure 15. Clustering Analysis of the Exposure Index in Queensland.
Figure 15. Clustering Analysis of the Exposure Index in Queensland.
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Figure 16. Clustering Analysis of the Sensitivity Index in Queensland.
Figure 16. Clustering Analysis of the Sensitivity Index in Queensland.
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Figure 17. Clustering Analysis of the Adaptive Capacity Index in Queensland.
Figure 17. Clustering Analysis of the Adaptive Capacity Index in Queensland.
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Table 1. Data Sources.
Table 1. Data Sources.
Data Source
Queensland boundary https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files
Mean surface temperature https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/102260
Mean precipitation http://www.bom.gov.au/climate/rainfall/
Population https://hub.worldpop.org/geodata/summary?id=49731
Building https://www.openstreetmap.org/export
Forests and nature reserves https://www.openstreetmap.org/export
Nighttime light radiation https://eogdata.mines.edu/download_dnb_composites.html
Table 2. Indicators used in the model.
Table 2. Indicators used in the model.
Primary indicators Secondary indicators Positive / Negative Note
Exposure Mean surface temperature (I1) Positive High temperatures are typically associated with adverse impacts of climate change, such as increased droughts, heatwaves, and extreme weather events.
Mean precipitation (I2) Positive Taking into account the significant climate variations among different regions in Queensland, the average rainfall is chosen as the benchmark. The greater the deviation from the average, the higher the likelihood of triggering extreme weather events.
Sensitivity Population density (I3) Positive A higher population density implies that more people are reliant on limited resources and infrastructure, such as food, water sources, housing, and medical facilities.
Building density (I4) Positive High building density can lead to issues such as inefficient land use, traffic congestion, and poor drainage, thereby increasing the risks faced during disasters and emergencies.
Proximity to forests and nature reserves (I5) Positive Forests and natural protected areas typically encompass diverse species. These ecosystems are more sensitive to climate change because their species composition and ecological interactions may not be able to adapt quickly to rapid climate fluctuations.
Adaptive Capacity Nighttime light radiation (I6) Negative Higher nighttime light radiation intensity typically indicates a higher level of economic development and infrastructure construction (Pan & Dong, 2021). This implies that the region has stronger adaptive capacity and resilience when facing climate change, thereby reducing vulnerability.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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