Application and Prospect of Geographical Information System in Strategic Environmental Assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".