Key indicators for Asia and the Pacific
Author(s)
Bibliographic Information
Key indicators for Asia and the Pacific
Asian Development Bank, 2022
- 2022, 53rd ed. : print
Available at 3 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
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  United States of America
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National Graduate Institute for Policy Studies Library (GRIPS Library)
2022, 53rd ed. : print332||A92||202210316880
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Nagoya Gakuin University Information Resource Center [Seto Campus]図
2022, 53rd ed. : print332.2/168/533000422829
Note
"August 2022"--Cover
Includes bibliographical references
Description and Table of Contents
Description
This publication provides updated statistics on a comprehensive set of economic, financial, social, and environmental measures as well as select indicators for the Sustainable Development Goals (SDGs).
The report covers the 49 regional members of ADB. It discusses trends in development progress and the challenges to achieving inclusive and sustainable economic growth across Asia and the Pacific. This 53rd edition looks at how most economies in the region have bounced back to varying degrees from the COVID-19 pandemic. A gradual recovery of cyclical industries, the release of pent-up consumer demand, and increased confidence levels have contributed to developing Asia's economy.
To put into practice the "leave no one behind" principle of the Sustainable Development Goals, detailed and informative data is crucial. The 2022 report features a special supplement, Mapping the Public Voice for Development-Natural Language Processing of Social Media Text Data, which explores how natural language processing techniques can be applied to social media text data to map public sentiment and inform development research and policy making.
by "Nielsen BookData"