Centre for Resource & Environmental Studies, Australian National University, Australia.
Environ Monit Assess. 1991 Jul;18(1):1-23. doi: 10.1007/BF00394475.
Our understanding of natural ecosystems can be measured by our ability to predict their responses to external disturbances. Predictions made during environmental impact assessment (EIA) for major development projects are hypotheses about such responses, which can be tested with data collected in environmental monitoring programmes. The systematic comparison of predicted and actual impacts has been termed environmental impact audit. Ecosystem disturbances associated with major resource developments, though of lesser magnitude than those associated with natural cataclysms, are generally of far greater magnitude than those which can be applied experimentally. Environmental audit can hence provide critical tests of theory in a number of natural sciences. It is also needed to improve the scientific content of EIA. Audits of 4 and 29 EISs respectively have been carried out previously in the UK and USA, but this is the first national scale audit for any country. It is also the first attempt to select, from the many vague statements in EISs, only those predictions that are scientifically testable, and to determine and analyse their quantitative accuracies. Its principal results are as follows. The average accuracy of quantified, critical, testable predictions in environmental impact statements in Australia to date is 44%±5% s.e. Predictions where actual impacts proved more than expected were on average significantly (p<0.05) less accurate (33%±9%) than those where they proved as or less severe (53%±6%).
我们对自然生态系统的了解程度可以用我们预测其对外界干扰的反应的能力来衡量。在重大发展项目的环境影响评估 (EIA) 中做出的预测是对这些反应的假设,可以通过在环境监测计划中收集的数据进行测试。对预测和实际影响的系统比较被称为环境影响审计。尽管与重大资源开发相关的生态系统干扰的规模小于与自然灾难相关的干扰,但通常比可以进行实验的干扰要大得多。因此,环境审计可以为许多自然科学中的理论提供关键测试。它也是改进 EIA 科学内容所必需的。以前在英国和美国分别进行了 4 次和 29 次 EIS 审计,但这是任何国家进行的第一次国家规模的审计。这也是第一次尝试从 EIS 中的许多模糊陈述中选择那些可进行科学测试的预测,并确定和分析其定量准确性。其主要结果如下。迄今为止,澳大利亚环境影响报告书中量化的、关键的、可测试的预测的平均准确性为 44%±5%(标准误差)。实际影响证明比预期更严重的预测的平均准确性(p<0.05)明显较低(33%±9%),而那些证明与预期一样或更严重的预测的准确性(53%±6%)。