We assess and compare different methods for including leading threshold logarithms at nextto-leading-power in prompt photon production at hadron colliders, for both the direct and parton fragmentation mechanisms. We do this in addition to next-to-leading logarithmic threshold and joint resummation at leading power. We study the size of these effects and their scale variations for LHC kinematics. We find that the next-to-leading power effects have a noticeable effect on the photon transverse momentum distribution, typically of order O(10%), depending on the method of inclusion. Our results indicate that next-to-leading power terms can reduce the scale dependence of the distribution considerably. † Deceased. We dedicate this paper to his memory. arXiv:1905.11771v1 [hep-ph] 28 May 2019 Recently, the SCET framework has been used to demonstrate that the leading-logarithmic (LL) NLP contributions to Drell-Yan production can indeed be resummed [59]. Preliminary studies [67][68][69] were performed for the resummation of a large class of leading logarithmic (LL with m = 2n − 1) NLP terms for direct production of prompt photons, in both threshold and joint resummation. In this paper we extend this work in a number of new directions. First, we now include the fragmentation mechanism, which requires additional colour structures in the hard scattering. Second, we assess different approaches to include initial and final state NLP terms and analyze them for both threshold and joint resummation. We furthermore examine the effect of including the NLP terms on the scale dependence of the resummed cross-section. Finally, we use the prompt photon process as a case study of NLP effects for a final state containing color-charged particles, providing numerical studies to assess the size of the NLP terms. The paper is organized as follows. In section 2 we review the threshold and joint resummed photon p T distribution, and discuss the inclusion of NLP effects for the initial and final state. In section 3 we assess the numerical impact of these corrections, and we conclude in section 4. In appendix A we collect explicit expressions for quantities listed in section 2, while in appendix B we compare the NLO expansion of our resummed expressions at NLP with exact results.