Co-creation of Sharable Visions among Diverse Stakeholders for Complex Social-Ecological System Management

地域環境知プロジェクト第1回国際シンポジウム，総合地球環境学研究所 講演室，2014-09-13，総合地球環境学研究所 地域環境知プロジェク

Socio-economic profiling of tropical rivers<br />

Summary of the reportTropical Rivers and Coastal Knowledge (TRaCK) consortium was established in 2007 under the&nbsp; Commonwealth Environment Research Facilities Programme with the aim of providing the science and knowledge that governments, communities and industries need for the sustainable use and management of Australia&rsquo;s tropical rivers and estuaries. This report has been written as a part of the Tropical Rivers and Coastal Knowledge (TRaCK) project 3.1, &ldquo;People and economy&rdquo;.Tropical rivers of Australia are defined as catchments stretching from Broome in Western Australia to Cape York in Queensland, draining into either the Timor Sea or Gulf of Carpentaria. Tropical rivers (TR) thus include 54 river catchments and cover an area of more than 1.3 million km2. Key characteristic of the tropical rivers, in general, is that their hydrology is determined&nbsp; by a short and distinct wet season, followed by longer dry season. While water might be abundant during the wet season, it is generally ephemeral and becomes scarce during the dry season.This document reports on four major objectives of stage (B) of the TRaCK project 3.1, &ldquo;People and economy&rdquo;. The four objectives were: (a) to develop an integrated conceptual framework for the socio-economic profiling; (b) to update existing knowledge with data from the 2006 Census; (c) to develop profiles of individual catchments based on their individual socio-economic characteristics;&nbsp; and (d) to compare and contrast the TR catchments and to identify catchments which are socio-economically &lsquo;similar&rsquo; or &lsquo;dissimilar&rsquo;.The conceptual framework developed for the study (objective a) was grounded in the social impact assessment theory but also reviewed literature from other related research areas, such as adaptive capacity, social resilience and institutional analysis. The original framework also included a &ldquo;wish list&rdquo; of variables that should ideally be populated in order to provide full profiles of&nbsp; socio-economic conditions across the north. The conceptual framework is presented in Section 2 of the report.The data collection and updating process (objective b) has identified several important data gaps, discussed further in Section 5.1 of the report. A comparison of the &ldquo;wish list&rdquo; of variables developed at the start of the project with the list of variables for which we were able to find recent, readily available secondary data, revealed some important data gaps. As a result, the&nbsp; conceptual framework originally proposed could not be populated to the full extent and was thus collapsed into the following five domains: (a) population / demographic characteristics; (b) economic parameters; (c) infrastructure and housing; (d) human and social capital, combining institutional arrangements with individual wellbeing; and (e) environment, heritage and land use. The data were used to create GIS-linked maps of socioeconomic characteristics of all catchments across the north (Section 3), as well as to create profiles of each individual catchment (objective c, presented in Section 4.1 and Appendix 2).Although the tropical rivers (TR) region represents around a quarter of the Australian land mass, it contains just two percent of the population. The region is characterised by disperse human settlement, with the only significant populations (more than 10,000 people) located in and around Darwin in the Northern Territory, Broome in Western Australia, and Mount Isa in Queensland. The&nbsp; Accessibility/ Remoteness Index of Australia (ARIA) indicates that large tracts of the TR region fall within the &ldquo;very remote&rdquo; category of ARIA as defined by ABS, with scores higher then 10.5.The percentage of people speaking a language other than English at home is relatively high in TR region. Most of the languages, other than English, which are spoken at home are Indigenous languages. And the percentage of the population speaking non-Indigenous languages at home is proportionally smaller (up to 15 percent per catchment) than populations speaking Indigenous languages at home (up to over 80 percent per catchment). For example, in Leichhardt River, only 74 percent of population was born in Australia, yet 82 percent speak only English at home. In contrast, the entire populations of both the Moyle and the Walker river catchments were born in Australia, yet only 12 and 10 percent, respectively, speak only English at home.About 42% of all homes in TR region have an internet connection, while 65% of all homes have a motor vehicle. In addition, catchments closer to urban regions such as Adelaide River (near Darwin) tend to have higher rates of internet connection and homes with registered motor vehicle (56% and 95% and, respectively), while more remote catchments tend to have a very low percentage of homes with internet connection (such as, for example, 20% in King Edward River or 11% in Fitzmaurice River) and a low percentage of homes with a registered motor vehicle (34% in King Edward River and 38% in Fitzmaurice). Similar disparity appears in human capital data. To use the same catchments as examples, less than one percent of people in the Adelaide catchment never attended school, while 3% of people in the Fitzmaurice and 5.5% of people in the Kind Edward catchment have never received any formal schooling.Combined government-provided services such as health, education, defence and public services were identified as the largest employer in the region, employing on average 25 percent of persons over 15 years of age in TR catchments in 2006. The second largest employment sector was agriculture and forestry, with an average of 11.5 percent across catchments, followed by mining, retail and construction, each employing around 4 percent of population over 15 years of age. Median weekly income per person varied greatly between catchments from around 150$per person per week in the Blyth and Koolatong catchments, to around 700$ per person per in mining-dominated catchments like the Leichhardt and Embley. The majority of the labour force in the catchments across the TR region was concentrated in the Darwin region. The few other catchments with larger settlements, such as Mt Isa, Broome and Katherine, dominate the remaining numbers of total labour available in the TR region and the labour force across the majority of other catchments is very limited suggesting that this might be one of the limiting factors for potential developments in the future.Basic infrastructure in the north is also limited. Transport infrastructure is limited to a weak network of all-weather sealed roads and airports, and very few ports. This is particularly true in the Kimberleys, Arnhem Land and Cape York Peninsula. Services are also limited to a few larger rural centres. For example, one third of 54 northern catchments profiled did not have any educational facilities. Similarly, the overwhelming majority of the community organizations across TR region registered in Australian Community Guide, 97 percent, were located within 10 catchments of the region.Much of the land in the TR region is in its natural condition, with most land use following within the categories of &lsquo;land under conservation&rsquo; &lsquo;traditional Indigenous use&rsquo;, and &lsquo;land under production from a relatively natural environment&rsquo; (such as grazing of natural vegetation). Other land uses, such as land under dryland agriculture, irrigated land and land under intensive uses, are minimal across the region. Great differences however do exist between the catchments. For example, all of the land in Goyder River catchment is classified as in natural condition (under traditional Indigenous use), while only 2.5% of land in Gilbert River is classified as being in natural condition (under conservation), with no land under traditional Indigenous use. The majority of land in Gilbert River catchment, more than 95%, is under grazing.To meet the last objective of this study, TR catchments were compared and contrasted in order to identify catchments which are socio-economically &lsquo;similar&rsquo; (and, by corollary, socio-economically &lsquo;dissimilar&rsquo;). It is important to note that, given the complexity of variation between the catchments, more good quality data is needed to reduce the uncertainty around the findings of this investigation. Nonetheless, distinct clusters of catchments have been identified and are discussed in Section 4.2 of the report.For example, Settlement Creek, Staaten, Keep, Gilbert, Holroyd and Norman rivers were grouped together as relatively similar. This cluster is characterised by relatively high levels of employment in agriculture and a high percentage of land under grazing. Mobility is also relatively high, with a large proportion of people owning their homes. A medium to high proportion of residents speak English only. Catchments in this cluster have low numbers of homes with no vehicles or no internet connection, and a relatively low percentage of people with no schooling. Household sizes and numbers of people per bedroom are also low, as well as the percentage of women with 3 children or more and the percentage of one parent families. The percentage of Aboriginal people in those catchments is low to relatively low.Another cluster identified in the analyses comprised of the Jardine, King Edward, Coleman and Watson rivers and Bathurst and Melville Islands. This cluster is characterised by a low mobility of population, low incomes and low employment in agriculture, manufacturing or mining. Employment by government is higher. In these catchments a low percentage of people are purchasing their homes, while most families are renting homes from the community organisations. An increased proportion of the population has no schooling. Catchments in this cluster also have medium to relatively high numbers of homes with no vehicles and no internet connection, and a relatively high proportion of women with 3 children or more. Household sizes and numbers of people per bedroom are higher than in the previous clusters described. The percentages of Aboriginal people in these catchments are medium to high, however, the percentage of land under Indigenous traditional use is not very high (except at Bathurst and Melville islands).In summary, the socio-economic profiling identified considerable differences both between and within the catchments in the north. Biophysical and cultural differences, as well as differences in human, social and institutional capital and available infrastructure, will play a large role in determining both the opportunities for development (mining, agriculture, tourism) as well as capacities of the communities in those catchments to identify opportunities and take the advantage of the opportunities as they present themselves.This study summarised data that might be of help to other researchers and communities in the north engaged in development of sustainable use and management options for the tropical rivers. Furthermore, identification of different types of catchments, that are not necessarily geographically linked but are similar in socio-economic terms, might aid in development of the management approaches that are more targeted, and thus more appropriate, than &ldquo;one size fits all&rdquo; approach; yet require lesser effort than targeting of individual catchments. Potential of this approach to be used for improved understanding and management of natural resources issues in other rural and remote regions of Australia warrants further research. Research into development of catchment typologies based on entire sets of data, that is biophysical characteristics as well as socio-economic characteristics of the catchments, also warrants further research

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM) predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process

Assessing Cognitive and Social Attitudes toward Environmental Conservation in Coral Reef Social-Ecological Systems

This study addresses the latent construct of attitudes toward environmental conservation based on study participant&rsquo;s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N = 945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based, and SEM-based) were consistently and reliably valid measures of the study participants&rsquo; latent attitudes toward conservation. We found statistically significant differences among participant&rsquo;s attributes relating to socio-demographic, physical, and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM) predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process