Nianyong Wang

Prof. Dr. Nianyong Wang

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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NWNianyong Wang
Nianyong Wang
MSMUHAMMAD HAROON SHAH
MUHAMMAD HAROON SHAH
KAKishwar Ali
Kishwar Ali
SAShah Abbas
Shah Abbas
SUSami Ullah
Sami Ullah
This study empirically analyzes the impact of the financial structure and misery index on economic growth in Pakistan. We adopted Autoregressive-Distributed Lag (ARDL) for a co-integration approach to the data analysis and used time series data from 1989 to 2017. We used GDP as the dependent variable; the Financial Development index (FDI) and misery index as the explanatory variables; and remittances, real interest, and trade openness as the control variables. The empirical results indicate the existence of a long-term relationship among the included variables in the model and the FD index, misery index, interest rate, trade openness, and remittances as the main affecting variables of GDP in the long run. The government needs appropriate reform in the financial sector and external sector in order to achieve a desirable level of economic growth in Pakistan. The misery index is constructed based on unemployment and inflation, which has a negative implication on the economic growth, and the government needs policies to reduce unemployment and inflation.

4 January 2026 in Sustainability
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MLMingxu Li
Mingxu Li
GWGuanqi Wang
Guanqi Wang
YSYixuan Song
Yixuan Song
RLRuijing Luo
Ruijing Luo
NWNianyong Wang
Nianyong Wang
Green finance is increasingly expected to support decarbonization while enhancing productivity, yet evidence on whether green credit bonds raise green total factor productivity (GTFP) remains limited. Using panel data for 29 provincial-level regions in China from 2016 to 2023, we compute GTFP using a slacks-based measure Malmquist–Luenberger (SBM–ML) index and estimate two-way fixed-effects models. To address endogeneity, we employ a Bartik shift–share instrumental-variable strategy. We found that green credit bonds significantly increase GTFP, with gains driven mainly by technological change (TC) rather than efficiency change (EC). The effect is stronger in eastern and western regions, in provinces that are not low-carbon pilot areas, and in regions with stronger low-carbon governance orientation. Public environmental attention directly improves GTFP but dampens the marginal effect of green credit bonds. Mechanism analyses further indicate that the low-carbon transition of the energy mix (LCEM) is an important transmission channel. Overall, these findings suggest that scaling up and better targeting green credit bonds, alongside complementary governance and public scrutiny, can accelerate China’s transition toward higher green productivity. This provides sustainability-relevant evidence that market-based green finance can support decarbonization while sustaining productivity growth, contributing to long-term sustainable development.