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Application and Prospect of Geographical Information System in Strategic Environmental Assessment

2009· article· en· W1958591841 on OpenAlexvenueno aff
Hongtao Bai, He Xu

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsStrategic environmental assessmentMacroProcess (computing)Computer scienceScale (ratio)Geographic information systemGovernment (linguistics)GRASPManagement scienceSpatial analysisSustainabilityDecision support systemData scienceOperations researchEnvironmental resource managementEnvironmental impact assessmentGeographyEngineeringData miningEcologyRemote sensingEnvironmental scienceCartography

Abstract

fetched live from OpenAlex

Strategic environmental assessment (SEA) is a new and promising tool to evaluate the possible environmental impacts and other sustainability aspects of government policies, plans and programs as well as their alternatives, aiming at the effectively integration of environmental concerns into their decision-making processes. It is recognized that spatial issues are always evolved in the SEA process for the reason that the strategic decision-maker pays much attention to the geographical distribution of various impacts. The essential features of strategic decisions, such as the macro-scale on time and geography, complexity of spatial information and social-economic-environmental multiplex system, are leading to the insufficiency of traditional EIA-based methods in assisting strategic decision-making. SEA practitioners have to seek for more effective approaches to handle the spatial uncertainties. Geographical information system (GIS), integrating computer design and database technology, is able to gather, simulate, analyze and display spatial information effectively, and provides a technological support to the synthesized evaluation and quantitative analysis. Based on the statement of SEA conception and characteristics, the author employs a detailed discussion on the advantages of GIS in SEA process and finds that GIS can not only help SEA operators to grasp the macro-scale of time and geography of strategic decisions much better, but also provide visible and intuitionistic displays on the spatial information to make decision-makers and publics understanding and accepting final conclusions much easier. Meanwhile, the author indicates that GIS with various tools, simulation models and powerful spatial analysis functions, can contribute to a more scientific, synthetic forecast and cumulative effects evaluation; provide much more quantitative analysis and improve the reliability of SEA results. Finally, the challenges and prospects of GIS in SEA process are presented in this paper. Key words: Strategic environmental assessment; Geographical information system; Information display; Spatial analysis Financial supports from National Social Sciences Fund (NSSF) in China: Research on Circular Economy Based on Win-win of Economic Development and Ecological Environmental Protection (06&ZD029) are highly appreciated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.003
Scholarly communication0.0000.010
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.284
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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