Question

急！！！！求人翻译成中文！！！不要翻译软件

1. Introduction
Climate change presents significant challenges to society [e.g.[1,2]. Many have concluded that climate change is the most important problem facing humankind, and indeed all other life on Earth. The construction industry, which usually contributes 5–10% of national GDP globally, has a prominent role to play in meeting this challenge given that the built environment demands 40–50% of global resources and generates a proportional amount of waste [3]. Climate change adaptation is about human response to this challenge, thus mitigating the impacts of a warming environment [4]. A major contribution that the construction industry can still make is ensuring that decisions about built assets are balanced: feasible, in the national interest and as sustainable as possible. A multiple criteria decision analysis (MCDA) framework is normally advocated [e.g.[5,6]. There is a need amongst the built environment professions for a transparent understanding of the goals or preferences of multiple stakeholders that underpin optimal decisions. Further, given the uncertainty associated with these choices, there is a need for a dynamic framework that refines predictions by learning from experience with a view to improving future decision heuristics. Adaptive management (AM) is a powerful approach to reducing ecological uncertainty and improving the overall performance of many resource-based systems [7]. It has the potential to improve the expected net benefit of specific developmental initiatives. It is sometimes described as ‘learning by doing’. Recently Linkov et al. [8] suggested that AM be combined with MCDA to provide structured, clear decisions and allow for refinement of criteria goals and weightings based on feedback regarding actual project performance. Accelerated learning from experience assures that better decisions are made in the future. AM acts as an opportunity for continuous process improvement for MCDA. It is a structured, iterative process of optimal decisionmaking in the face of uncertainty, which aims to reduce uncertainty over time via system monitoring and learning. In this way, decision making simultaneously maximizes one or more resource objectives and, either passively or actively, accrues information needed to improve future management. One of the most successful applications of AM has been in the area of waterfowl harvest management in North America, most notably for the mallard [9,10]. When evaluating projects and facilities it is important to take a holistic view. Current practice, however, is primarily concerned with issues of profitability and the financial bottom line. This approach leads to decisions that are not necessarily compatible with wider social considerations and sustainable development goals. A new methodology is needed. John Elkington2 formally proposed the triple bottom line concept in 1997.

Answer 1

1。景区简介气候变化带来了重大的挑战社会[如[ 1 , 2]。许多人认为气候变化是人类面临的最重要问题，以及所有其他地球上的生命。建筑行业，这通常有助于5–国民生产总值10%在全球范围内，有一个突出的作用，发挥在应对这一挑战，建成环境要求40–50%的全球资源和生成一个成比例的废物量[ 3]。适应气候变化是人类应对这一挑战，从而减轻影响，气候变暖的环境[ 4]。一个重大贡献，建筑业仍然可以确保决定建造的资产的均衡：可行的，在国家利益，可以永续发展。一个多标准决策分析（民防）框架通常主张[例如[ 5 , 6]。有需要在建筑环境专业为透明的认识的目标或偏好的多个利益相关者的最优决策。此外，鉴于不确定性与这些选择，需要有一个框架，细化预测从经验中学习以提高未来的启发式决策。适应性管理（是）是一个功能强大的方法来减少生态环境的不确定性和提高整体性能的许多资源系统[ 7]。它有可能改善的预期净效益的具体的发展措施。它有时被称为“干中学”。最近林科夫等人。[ 8]表明，在结合民防提供结构化的，明确的决定，并允许完善准则及权重根据反馈关于项目的实际表现。加速学习经验确保更好地作出决定，在未来。是作为一个机会，持续的过程改进为民防。它是一种结构化的，迭代过程的最优决策中面临的不确定性，其目的是减少不确定性，通过系统监控和学习。在这方面，决策的同时最大限度地一个或多个资源的目标，无论是被动或主动，累积的信息需要改善管理的未来。最成功的应用之一是已在该地区的水禽收获管理在美国北部，最显着的野鸭[ 9 , 10]。当评价项目和设施，重要的是要采取全面的观点。目前的做法，然而，主要关注的是问题的盈利能力和财务底线。这种做法导致的决定，不一定与更广泛的社会因素和可持续发展的目标。一种新的方法是必要的。约翰elkington2正式提出的三重底线概念1997。