This article explores the role of sustainability reporting and governance in achieving national sustainable development goals. Sustainable development goals focus on economic, societal, and environmental issues and have been set up to address issues regarding environmental degradation, global imbalances, economic instability, social instability, and political instability. Using data from 42 countries over six years, the authors apply panel regression techniques and find a positive relationship between national governance and sustainability reporting; sustainability reporting and national sustainable development goals; sustainability governance and sustainability reporting. We found a negative relationship between national governance and sustainable development goals. Sustainability reporting is also found to mediate between national governance and sustainable development goals. Thus, this paper contributes to the body of existing knowledge by highlighting the role of governance and sustainability reporting in the achievement of sustainable development goals. The findings have several implications for governing bodies and decision-makers in government, including changing the governance model and taking strict actions against companies that fail to focus their attention on sustainability reporting. The findings involve society, business, and other stakeholders in sustainability reporting measures to achieve sustainable development goals.

Purpose: The purpose of this study is to specify an efficient forecast model for the accurate prediction of macroeconomic variables in the context of Pakistan. Design/Methodology/Approach: We particularly investigate the comparative accuracy of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models-based predictions using monthly data of inflation, exchange rate, and GDP from 1990 to 2014. Findings: According to our findings, the ANN-based forecasted inflation series is more precise as compared to ARIMA-based estimates. On the contrary, the ARIMA model outperforms the ANN model for exchange rate forecasts with the forecasted values being very close to the actual values. Further, ARIMA performs comparatively better in forecasting GDP with relatively smaller forecast error. On the whole, our findings suggest the ARIMA model provides appropriate results for forecasting exchange rates and GDP, while the ANN model offers precise estimates of inflation. Implications/Originality/Value: Our findings have important implications for the analysts and policymakers highlighting the need to use appropriate forecasting models that are well aligned with the structure of an economy.