This study investigates the effect of audit quality on firm investment efficiency for 125 French-listed companies over 2008–2015. It uses parametric and non-parametric measures of firm investment efficiency, based on residuals extracted from the investment efficiency model and the data envelopment analysis (DEA) approach, respectively, to assess whether audit quality improves investment inefficiency. It analyses this relationship after distinguishing between firms that under-invest and those that over-invest. The results show that investment inefficiency decreases with audit quality. Specifically, auditor knowledge leads to less investment in firms prone to over-investment and more investment in firms prone to under-investment. This relationship appears to be independent of a firm’s financial reporting quality, which indicates that auditors provide value-added services that impact the investment decisions of firm managers, separately from the quality of accounting information.

Urban solid waste (USW) is a major environmental problem for all countries. The rapid acceleration of economic growth, industrialization, urbanization, and population growth is correlated with the production of solid waste. Solid waste is usually disposed of in solid waste collection bins, so estimating the potential number of solid waste collection bins is necessary to plan an efficient USW management system. In this article, the purpose is to forecast the solid waste generation based on the monthly recorded amount of waste to determine the appropriate number of waste bins. Several artificial intelligence regression and classification approaches have been evaluated for their efficiency to estimate the number of bins. We highlight the effectiveness of sequential models, namely, long short-term memory (LSTM) and bidirectional LSTM (BLSTM), as waste data commonly consist of real-valued time series. Our experiments demonstrate the performance of the LSTM and BLSTM models, in terms of the number of waste bins prediction, when compared with other methods.