เปรียบเทียบวิธี

	Backcasting for Policy	Scenario Planning for Policy
สาขาวิชา	Public Policy	Public Policy
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1990	1991
ผู้ริเริ่ม	John B. Robinson (building on Amory Lovins' energy work)	Pierre Wack & the Royal Dutch/Shell school; popularised by Peter Schwartz
ประเภท	Normative futures and foresight method	Qualitative strategic-foresight method
แหล่งต้นตำรับ	Robinson, J. B. (1990). Futures under glass: A recipe for people who hate to predict. Futures, 22(8), 820–842.	Schwartz, P. (1991). The Art of the Long View: Planning for the Future in an Uncertain World. New York: Doubleday/Currency. ISBN: 9780385267311
ชื่อเรียกอื่น	Backcasting, Policy Backcasting, Normative Scenario Backcasting	Scenario Planning, Intuitive Logics Scenarios, Policy Scenario Planning, Strategic Foresight Scenarios
ที่เกี่ยวข้อง	4	4
สรุป	Backcasting is a normative futures method that starts from a desirable future end-state and works backward to determine the policies, actions and milestones needed to reach it from the present. Coined and developed by John Robinson, who set out its logic in his 1990 article 'Futures under glass', it deliberately contrasts with forecasting: rather than asking what future is likely given current trends, backcasting asks what future we want and how we could get there. It is especially suited to long-term, transformative challenges such as sustainability and decarbonisation, where prevailing trends point away from where society needs to go.	Scenario planning is a strategic-foresight method that develops a small set of plausible, internally consistent and divergent stories about how the future might unfold, in order to test policies and strategies against deep uncertainty. Rooted in the work of Pierre Wack at Royal Dutch/Shell and popularised by Peter Schwartz's The Art of the Long View, it does not try to predict the future but to expand decision-makers' thinking about it. By exploring several qualitatively different futures, policymakers can craft strategies that are robust across a range of possibilities rather than optimised for a single forecast that may not arrive.