In conventional causality testing based on asymptotic distribution theory, there is a high risk of wrongly rejecting the true null of no causality especially when the sample size is as small as typically seen in the literature. In this study, we offer a formal diagnosis of the existing contradictory results on the causal relationship between energy consumption and real GDP. We also employ a time series oriented advanced data generation process to perform simulation based inference for the People's Republic of China. Our study covers the 1971-2007 period and considers five different aggregated and disaggregated energy consumption measures as well as three different lag orders in both a bivariate as well as a multivariate frameworks. Our maximum entropy bootstrap based analysis, which avoids pretest biases and is also robust to Type 1 errors, supports the neutrality hypothesis in 53 out of the total of 60 model estimations. The strong results show that coarse aggregate data has a limited potential to observe the complex causal linkages between energy consumption and economic growth. Future policy oriented research on this nexus requires more focused analyses based on sectoral and provincial data. (C) 2011 Elsevier Ltd. All rights reserved.