Spatial and temporal assessment of sustainable development indicators for the China-Pakistan transportation corridor

ABSTRACT

The China-Pakistan Transportation Corridor (CPTC) has long faced great challenges in natural disasters and sustainable development. This study develops a localised indicator evaluation system for the CPTC sustainable development. It contains 27 Tier I and 8 Tier II indicators, covering 11 sustainable development goals. Based on this evaluation system, a single-goal, multidimensional, and comprehensive evaluation of the CPTC sustainable development level is conducted for 2015, 2017 and 2020. The results show that, in terms of single goals, Xinjiang has the highest sustainable development level. Khyber Pakhtunkhwa has the poorest level of sustainable development. The Islamabad Capital Territory is better developed for all goals, especially in SDG5, SDG8 and SDG9. Besides these three goals, SDG6, and SDG11 are more prominent in Punjab, whereas SDG2 and SDG3 performed worst. From a multidimensional evaluation, the CPTC sustainable development level has been steadily increasing. However, the social and environmental dimensions of Khyber Pakhtunkhwa have experienced a ‘regression’. The comprehensive evaluation results show that the level of sustainable development in all provinces of the CPTC increase, except for Khyber Pakhtunkhwa. This study identifies CPTC sustainability issues and proposes targeted recommendations for sustainable development.

KEYWORDS:

• Sustainability indicator evaluation system
• sustainability evaluation
• China-Pakistan transportation corridor
• localisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Publicly available datasets were analysed in this study, specific data sources can be found in Tables 1 and 2.

Additional information

Funding

This work was supported by the National Key R&D Program of China (2022YFF0711600); the Construction Project of the China Knowledge Centre for Engineering Sciences and Technology (CKCEST-2023-1-5); the Natural Science Foundation of Jiangsu province named as ‘Hyperspectral inversion of photosynthetic and physiological indicators for Suaeda salsa based on UAV in coastal wetland under water and salt stress’ (BK20221397).